text:
## ABSTRACT

During the late 1960s and early 1970s the University of Illinois took many risks in an effort to diversify the ranks of their student body. One such initiative was the Carnegie Scholars experiment; thirty minority students were recruited to the Graduate School of Library Science, twenty-nine graduated, and many went on to become leaders in their profession. The experiences of these Scholars, told through archival records, can inform current discussions about the recruitment and care of minority students in library and information science graduate programs, and in higher education in general. The program was described as “an unusual, flawed, but ultimately successful program to increase the number of disadvantaged students, primarily black and Hispanic” in the profession. The program was shortsighted, fraught with miscommunication and low expectations, and a definite product of the racial climate of the time.

This study portrays a significant portion of the story of these thirty minority Scholars as they are depicted in archival records. The archives suggest that the Scholars experienced difficulties at the then–Graduate School of Library Science (gsls). A combination of evaluation cards (performance reports), grant applications and reports, and student application materials intimate that these Scholars may have been “guests in someone else's house,” a not uncommon phenomenon, discussed in the literature of higher education. In this body of literature it is well documented that students of color experience varying and low levels of comfort at predominantly white institutions of higher education (pwis). This discomfort is one of several factors that influence the ultimate retention and academic success of students of color. Such students will remain in school “when he or she feels connected, involved and served.”2 There needs to be a quality and substantive connection between the student and the institution in terms of both academic interest and personality. Students of color do not always find this connection, and this process can be even more difficult in graduate school.

This scenario brings to bear the metaphor of being a (unwelcome) guest in someone else's house. The lack of connection and subsequent discomfort contribute to feelings of being unwelcome and wanting to leave the problematic environment. “We feel that we're a guest in someone else's house, that we can never relax and put our feet up on the table.” The metaphor continues by implying these unwelcome guests have no history and little in common with their “hosts,” their customs, possessions, and familiarities are not found in the house, and “guests are not family, whose foibles and mistakes are tolerated. On the contrary, guests must follow the family's wishes without question, keep out of certain rooms in the house, and always be on their best behavior.”3 All of these barriers serve as encouragement and rationale for the guests to leave the house.

These circumstances that render guests uncomfortable and unwelcome in their hosts' home are tantamount to those experienced by students of color. Campus climate issues at pwis and, subsequently, the climate of individual schools, colleges, and departments can be hostile, unwelcoming, demonstrate a distinct lack of support or isolated support for students of color, and suffer from a lack of representative faculty and staff. As a result, the recruitment and retention of students of color remains a challenge for institutions of higher learning. This challenge is unfortunately not new, and has been acutely evident and studied in higher education at large and in library and information science for decades.4

This examination of the Carnegie Scholars is an example of the collision between good intentions, entrenched systems of oppression, a volatile national climate, and a flawed higher education landscape. Recounting a historical case study, such as the story of the Carnegie Scholars, is an important step toward fleshing out the history of librarianship and library education, and capturing otherwise forgotten episodes. Such historical case studies are also important as they allow current educators and practitioners to learn from both past missteps and successes and apply them to current efforts and initiatives.

## Historical Context for the Study

On May 17, 1954, US Supreme Court Justice Earl Warren delivered the unanimous Supreme Court ruling that determined that state-sanctioned segregation of public schools was a violation of the Fourteenth Amendment and therefore unconstitutional. This historic decision marked the end of the “separate but equal” precedent set by the Supreme Court nearly sixty years earlier in the Plessy v. Ferguson decision, which upheld a Louisiana statute that required segregation of railway carriages and served as a catalyst for the expanding civil rights movement during the 1950s.5

Educational equality in the United States, or the lack thereof, has a long and difficult history. The notion of “separate but equal” served as a de facto justification for segregation in schools and within society at large, and the Brown v. Board of Education ruling in 1954 and the 1964 passage of the Civil Rights Act, which banned discrimination in voting, public accommodations, education, and employment, did not magically or automatically alter the status of minorities in the country. The presence and legacy of segregation enabled many decisions to be made that ultimately preserved and empowered the privilege and power of the majority, decisions that negatively shaped the educational landscape.6 Figa and Macpherson detail another thirty plus years of court cases that fought to eliminate racism and discrimination in higher education, after 1970.7 “The need for legal pressures and extended litigation regarding institutional obligations to equitably serve a more diverse group of students has conveyed the message of institutional resistance and, in some cases, outright hostility toward people of diverse backgrounds.”8

The normalization and perpetuation of racism also impacted libraries, particularly those in the southern states, as has been documented in the library literature.9 Racism also had a very significant role in library and information science education. For many years African American librarians were refused admittance to graduate library programs at predominantly white institutions around the country. Dumont recounts that “blacks were denied admission to southern white colleges and universities; as a result of the Second Morrill Act they were relegated to a separate system of federally aided land grant colleges; and because of inferior education in segregated elementary and secondary schools, they entered higher education woefully ill prepared. Consequently, blacks were very slow in entering the ranks of professional librarians.”10

In a 1929 American Library Association survey to library school directors, some of the responses were bold and unequivocal in their refusal to admit African American students to their program. The director of Pratt Institute stated, “No Negro applicant has ever measured up to the standards of the School of Library Science and none has, therefore, been admitted.” The director at the University of Michigan stated, “My own impression is that the school at Hampton Institute should be quite sufficient to provide such colored librarians as are needed for some time to come. It seems to me that the Board of Education will do far better to send students to Hampton rather than urge them to come to other institutions where their presence is a distinct embarrassment.”11 As a result of this not uncommon sentiment, aspiring African American librarians were trained predominantly at the Hampton Institute Library School (1925–39); the Negro Teacher-Librarian Training Program (1936–39); the Atlanta University School of Library Service (which was established in 1941 and merged with Clark College in 1988 to form Clark Atlanta University, whose library program shut down in 2003); a high school apprenticeship program in Louisville, Kentucky (1910–1930s); and the North Carolina Central Library School (which was established in 1941 and is now part of North Carolina Central University).12 By 1970, when the Title II-B Fellowship of the Higher Education Act of 1965 was enacted, African Americans and other minority students were eventually accepted at many other graduate library programs around the country, but that doesn't mean their experiences were easy or equitable.13

Much like other parts of the United States grappling with the legacy of the Brown vs. Board of Education decision and the volatile consequences of race riots and the civil rights movement, in the 1960s colleges and universities in the state of Illinois had embarrassingly few people of color studying on their campuses. In 1967 there were only 372 black undergraduates attending the University of Illinois, out of 30,400. The disconnect was noticed, as was the opportunity for federal and foundation funding, and the university launched an initiative called Project 500, the goal of which was to enroll 500 black students in the next incoming freshman class.14 This was the university's first “overarching university-wide program” dedicated to increasing diversity and the ranks of minority students.15 The program's main goal was to facilitate the advancement of low-income minority students through the educational system, and improve the racial diversity and campus climate at the university. The stated mission of the program reads:

The university put forth five objectives in implementing seop: (1) to provide an educational opportunity for students who may not otherwise have had the opportunity to attend college; (2) to increase the number of minority students on the campus; (3) to develop education programs and practices to aid the disadvantaged students in their academic careers; (4) to expose non-seop students to the cultural and social experiences necessary in understanding different cultures; (5) to develop information to deal successfully with educational and sociological problems affecting students from disadvantaged backgrounds.16

The target of Project 500 was achieved the very next year in 1968 when the university admitted 565 black students (502 freshman and 63 transfer students). This was a significant, revolutionary, and controversial development for the University of Illinois, and other institutions around the country were watching the outcome of this social experiment.17 The initiative's first director, Clarence Shelly, sent out “a team of student recruiters” who were largely unprepared for their weighty tasks and “spent the summer of 1968 traveling the country in search of students who, under normal circumstances, would not have been able to attend the University.”18

In 2008, the fortieth anniversary of Project 500, one of the 565 students, Nathaniel Banks, recalled, “This campus was a lot different in 1968,” he said. “It was very homogenous, mostly affluent white students from northern Illinois, so there was a degree of hostility at that time.” Banks also described the campus climate as antagonistic. Initially treated well and promised a good campus experience, once the national spotlight receded these minority students found themselves feeling like unwelcomed guests as their financial aid dwindled, they were placed in remedial classrooms, they faced physical and verbal attacks, and housing discrimination became blatant. Protests and arrests ensued, feelings were hurt, and corrections had to be made. “It was a very uncomfortable time,” and there was an “atmosphere on campus that was disdainful towards the new black students,” Shelley recalled forty years later.19 “We assumed we could integrate 500 students who were very much unlike the student body without trauma, misunderstandings, mistakes and flaws. We didn't appreciate how deeply ground the university was in its culture and history.”20 To say that this was a painful period of growth would be an understatement. Ultimately the university was compelled to offer additional support to students, resulting in “approximately one-third of the seop students” graduating by 1973.21 Of the students who had not completed their degrees at that time, 15 percent returned to the campus and finished their programs and 27 percent transferred to other institutions to complete their degrees. Additionally, the students who christened this initiative formed strong bonds and friendships in their cohorts, undoubtedly a contributing factor in their endurance at the university. The Carnegie Scholars program was a direct outgrowth of the Project 500 initiative, and unsurprisingly experienced many of the same frustrations and difficulties as its “parent” program.

## The Story in the Documents

Spurred by that photo of the Carnegie Scholars in the gslis magazine, many questions came to the forefront, particularly since their tenure at the library school is not well documented. The goal of this research was to investigate the following questions: What was the experience of these Scholars at gsls during this particular period in time? And, what, if anything, does their experience tell us about the recruitment and retention of students of color in lis? While also briefly examining the context of the time, and situating the data in the political, social, environmental, and cultural events of the time period, this study paid careful attention to original primary sources about the Carnegie Scholars and sought to corroborate them with secondary sources.22 Data were collected primarily from the University of Illinois Library's archives and institutional repository; examined materials included 3 student directories, 28 student id cards, 427 evaluation cards, 29 admissions applications, 2 sets of grant applications and reports, internal memos, and several published articles by Dr. Terry Crowley. The archival data were analyzed for emerging themes using grounded theory techniques as established by Charmaz, and the data were coded using open-coding techniques to identify emergent themes.23 The researcher used these themes to categorize the comments derived from the grade cards and collocated them with the data from the additional primary sources. From this process the researcher developed a narrative from the data that traces the students' experiences through their journeys as Carnegie Scholars.

In 1970 the Carnegie Corporation of New York awarded the library school $65,000, which supported the first class.24 The remainder of that funding, plus another grant from the US Department of Education, funded the second class. This funding supported thirty students, covering their tuition and providing them with a generous stipend. Similar to the language used in the rationale and recruitment of the students for the Project 500 initiative, the grant application to the Carnegie Corporation of New York, student recruitment materials, the students' application materials, and subsequent grant reports stressed the importance of serving “disadvantaged” and “underprivileged” students.25 The program sought students from “small, academically weak” schools, specifically “predominantly Negro colleges of the South” and “big city public colleges” (schools in large urban areas or isolated black colleges in the south were viewed as natural and untapped sources for minority students) who had students who would be able to clearly demonstrate their “disadvantaged” backgrounds, emphasize their “minimal” academic preparation (and consequently their need for remedial work), and describe how they were “socially or culturally deprived” in their application materials. It should be reiterated that these students were applying to a graduate program and as such needed to be successful college graduates prior to admission. All of the Scholars met the requirements of the graduate college, which included a 3.5 grade-point average.26 Consequently, the insistence of this forced derogatory labeling was an initial barrier and consistent source of contention for the Scholars. Students in the first cohort were also asked to take diagnostic tests to ascertain the level of remedial work required.27 When this group came to campus in the summer before their program began, they were enrolled in reading and writing clinics, the course content of which was assumed to be at the eighth-grade level, certainly not appropriate for college graduates, some of whom had substantial work, military, and even library experience.28 The students balked at this treatment and assumption of their lack of intellectual prowess; several students complained to Crowley and the school's director and, as a result, one student refused to attend the remainder of the summer classes. Eventually this component was removed from the program, and the second cohort did not have to take these remedial courses. This particular instance set into motion a tenuous relationship between the Scholars and the program and unfortunately revealed some differences among the students (e.g., those students who complained about the remedial courses and those who did not for fear of causing trouble). To his credit, Crowley acknowledged the faults of the program and its assumptions, and upon reflection made the following observation: It became clear to the director that something was seriously wrong with the assumptions of the program, that these college graduates were not about to allow themselves to be treated like “freshman” and that the selection criteria had worked to produce a strong, cohesive cadre of students who were not the pliable, submissive, compliant individuals we had expected.29 It was only after this tumultuous time with the Scholars did the program realize that the students were an asset to the entire graduate program, should not be separated from the rest of the student body, and were not a liability or poor group of minority students to be rescued, saved, and propped up. Crowley also acknowledged a lack of faculty support for the program, which was abundantly evident in the archives, specifically in some internal memos and in the evaluation reports filed about the Scholars. Crowley noted that previous school and community efforts to recruit minority students were unsuccessful and not supported by the school's faculty; the Carnegie Scholars were also not well received. The faculty as a whole “[did] not feel they can support” the statement on minority recruitment or make a statement “committing themselves to oppose racism in society or in our profession”; there was no need for organized recruitment, and the Carnegie program was “experimental” and “something apart from the regular library school program … to be handled entirely by its director.” The school could not “commit itself to further action in minority recruitment until the results from the Carnegie program” were available.30 Also admitting an error in assumptions about the Scholars, the director of the library school at the time, Herbert Goldhor, said, “Some very talented students, we have discovered, are to be found in this group; their leadership and academic strength are important in group dynamics. It is also important for the faculty to know that the program has students with considerable academic potential.”31 The students' forced “disadvantaged” status (which was overemphasized and even embellished to secure funding) did not equal academic or social deficiency. ### Crowley's Grant Applications In the proposal entitled “A Program to Prepare Disadvantaged Students for a Career in Librarianship,” Crowley formally requested funding from the Carnegie Corporation of New York to prepare disadvantaged students for a career in librarianship, students who experienced “economic or educational deprivation.”32 The goals of the initiative were as follows: Twenty students who are members of educationally and economically deprived minority groups will be accepted for participation in the proposed institute. Aptitude and motivation for librarianship are important, but we seek candidates whose backgrounds indicate a lack of educational opportunity. The program is intended to compensate for academic deficiencies of participants (1) by varying course loads according to each student's adaptability to graduate study, (2) by allowing any participant up to two years of full-time study to complete requirements for the degree, (3) by offering students remedial instruction and tutorial aid when needed, (4) by providing special counseling for students when desired, (5) by attempting to integrate participants into the courses and student activities of the regular M.S. program, (6) by providing students with financial support, (7) by giving participants library work experience if needed, and (8) by helping a student find employment after earning the degree.33 In order to qualify as “disadvantaged” students were required to meet the following criteria: First, of course, such students must meet the criterion of being “disadvantaged.” They must show that neither they nor their families have the resources to contribute financial support to their continued education. They will also be asked to file confidential statements about their personal history, which indicates a socially or culturally deprived background. In addition, they must be students whose academic qualifications for admission are minimal and, therefore, would not normally qualify them for a graduate stipend, which is traditionally awarded on the basis of scholarly achievement and potential rather than need.34 The constant referencing of the students as “disadvantaged” proved to be problematic for the gsls faculty and staff, some of whom later retracted their false and hurtful assumptions of the Scholars, and for the students themselves who ultimately felt disenfranchised and “less than” by this label. The academic backgrounds of the Carnegie Scholars, as indicated in their application files and the requirements of the Graduate College, did not suggest that these Scholars were in any way deficient, nor were there outright indications that they were socially deficient or culturally deprived. Their applications and accompanying letters of recommendation suggest that everyone involved recognized the necessity to use such labels even if they weren't accurate, with the term “disadvantaged” placed in quotes. As with the terminology and assumptions surrounding the Project 500 initiative, these were the words and stereotypes used when recruiting minority students to college campuses, in order to secure funding and to fall in line with the biases of the day. Additionally, per the program's stated goals, the grant application sought extra support for students to take four and half semesters to complete their course of study, instead of the typical two and a half semesters, the assumption being these students would need extra time to complete the academic program.35 The stated rationale for this request was: “Normally, students can complete the master's degree in three terms, but this represents a relatively heavy load—much too heavy perhaps for the disadvantaged participants in this program.”36 Again, the grant application made assumptions to correspond with terminology of the times and perpetuated assumptions used to paint the picture of students who essentially weren't capable of graduate school and were going to be saved and turned into professionals by this new program. Finally, the grant requested funding in order for students to begin their programs in the summer with an extended orientation entitled an “Institute,” an introduction to library science seminar, reading and writing clinics, and optional counseling.37 Once the program officially began in the fall, Scholars would participate in field trips, benefit from guest lecturers, and be assisted with both summer internships and job placement. In terms of financial assistance, students were well cared for; the first eleven Scholars received two tax-free stipends,$450 in the summer and $2,200 during the year, plus their full tuition and fees were covered (room, board, and incidentals were to be paid out of their stipends). In planning for the second year of the program, Crowley identified “a lack of support and outright resistance on the part of the majority of the faculty” and therefore sought more outside support, namely from the US Office of Education who awarded the program a grant of$75,000 (the original amount was slated to be $145,775), which supported the second cohort of nineteen Scholars. Stipend amounts were raised to$600 and \$3,000 respectively for this group.38

Three items of interest from the original grant application warrant special notation. First, in the grant proposal Crowley stated: “The director of the institute will work to help every participant achieve independently his educational objectives.”39 It cannot be known if Crowley knew just how important he would be to the Scholars or if this was a throwaway line added to give the proposal gravitas. The archives about the Scholars contain a few random memoranda from program instructors, directed to Dr. Crowley, in which the Scholars were complained about (in such a manner that it appears they took relish in targeting this group of students), and in his return correspondence, Crowley was firm in his correction of his colleagues and steadfast in his praise and support of the Scholars. It is without exaggeration to say that he made sure his Scholars successfully completed their courses of study and sent them away from the program with the confidence that they would indeed be successful librarians.

Second, an instance during which Crowley heard the Scholars' complaints and concerns was in regards to the summer reading and writing clinics. Referenced in the program's goals (point number 3) that stated that students would have access to “remedial instruction and tutorial aid when needed.”

Early in the program, participants who indicated a need were given the opportunity to develop study skills in order to take full advantage of graduate courses at the University of Illinois. Tutorial assistance in writing improvement and orientation in the use of the University Library were offered to those students. Our experience with scholarship students had shown the error of using standard criteria to judge deficiency in academic skills.40

In actuality, this was not optional and all of the students in the first cohort were compelled to take these courses. When the students complained about being forced into “remedial” coursework, Crowley could not stop the offending courses immediately; however, such remedial instruction was eliminated from the program and the second cohort did not have this same experience. In this way, Crowley heard the Scholars and worked with them toward the improvement of the program and their own experiences.

Third, despite Crowley's enormous efforts, there was an unsuccessful aspect of the program; in the initial statement of program goals, Crowley wrote that the Scholars would be “integrated into the courses and student activities of the regular M.S. program.” There were instances of successful, episodic, integration, but on the whole, the Scholars were isolated from the rest of the student population. Perhaps this was another unavoidable consequence of being a student of color at the University of Illinois in the early 1970s, and being a “disadvantaged” minority student at a pwi.

### Evaluation Cards

The most significant sources of archival data about the Carnegie Scholars were evaluation cards (205 cards were examined). Tantamount to report cards (although the evaluations did not indicate final course grades), these cards were filled out by faculty and instructors for every class the Scholars completed. In a partial archival file containing two of the Scholars' evaluations of the program, the students stated that the curriculum lacked minority perspectives, remedial classes they were forced to take were unnecessary and insulting, and that the faculty were neglectful and projected negative attitudes toward them.41 Specifically, faculty “persisted in the idea that minority students in the program were incapable of competing on an equal basis with non-minority students even though their academic backgrounds and performance in school indicated otherwise.” This statement is a clear indication of the lack of integration of the Scholars into the wider life of the school, and that the Scholars did indeed feel like guests in someone else's house (and possibly even unwelcomed guests). These sentiments were corroborated in the evaluation cards.

There were two versions of evaluation cards, one of which was entitled “Faculty Rating of Student Performance,” which asked for the course title and number and asked the instructor to rank the student (superior; above average; average; below average; or no basis) on the following qualities: appearance, manner, ability to work with others, initiative originality, effectiveness in speaking, effectiveness in written expression, and performance in the course. The card also asked the instructor for general “observations.”42 A second version of this card is more open-ended and requests the following: “Please comment on qualities which characterized the student's performance in your class and/or qualities which might indicate his potential for a successful career in librarianship. A student's appearance, personality and general manner may be noted, as well as his creativity and effectiveness in speaking and writing.” The origin and usage dates of the evaluation card are unknown, and it is also unknown why there were two versions of the evaluation cards, or why the cards were designed to elicit personal and subjective comments, such as those about the students' appearance.

While there were many positive and constructive comments that relate to the Scholars' abilities and professional promise, there were many negative and unnecessary comments. There was also a substantial category of comments that are considered ambiguous (because they are both positive and negative), comments that might be best described as backhanded compliments. It is clear from the evaluation cards those members who were generally supportive of the Scholars and those who were antagonistic toward these students. Several instructors were consistent in their praise and constructive criticism of the Scholars, just as the negative and ambiguous comments consistently came from select others on the faculty. That one of the Scholars said that some members of the faculty were neglectful and projected a negative attitude toward the minority students was a seemingly accurate assessment of the environment.

The comments represented here in the following categories cross all ranks of the faculty, cover required and elective courses, and represent courses in the various subject areas taught in the program (e.g., youth services, audio visual librarianship, management, reference services, and so on). During the period 1970–72, the school had twenty-three full-time faculty and several adjunct instructors, and utilized specialized instructors from other parts of the university, many of whom taught the Carnegie Scholars. The fulltime faculty members taught the majority of the courses during this period.

The positive comments make no reference to race or physical characteristics; comments are unanimously related to the students' academic performances. Positive evaluation card comments about the Carnegie Scholars included:

She is pleasant to work with. Has real promise. Always questions when necessary and if she's not certain about the answers she pursues the questioning.

Knowledgeable, bright student who does well in what interests her; she should go far.

She is a mature, animated, vivacious student who has a good deal of motivation (despite her low grades).

Her strongest asset is a thoroughly professional attitude.

Nothing is going to keep her from crashing to the top. She knows what she wants, and is willing to work hard to get it. At this point I don't think she has the faintest idea what work in a library is all about, but I expect her to learn in about ten minutes. This pattern will occur throughout her career.

It is gratifying to see these positive comments about some of the Scholars, but these comments were unfortunately not the norm. Another positive comment was:

A mature, serious, capable student. Quite personable and popular among fellow students, black and white. A sure success professional. Interested and ambitious.

While this comment is positive, it is a salient reminder of the distinctions made between students—black and white—which confirms the assertion that the Scholars were not fully welcomed or integrated members of the larger school community.

Ambiguous comments, or backhanded compliments, include:

A clear picture of her personality is hard to discern, but one suspects that she is extremely strong in interpersonal situations, with abilities to be directed and controlled by a strong, imaginative, and venturesome supervisor.

Good, easy-going young man, likable. A poor student, but he can get by without working too hard, and he will settle for that.

Delightful appearance, general manner good, tho sometimes seems moody.

She handles herself very well in a political situation. Her background is a bit thin but there is enough dramatic talent to fake her way through very nicely.

The implications here are that these students were nice enough people, but perhaps not as smart, personable, or professional as other students. These comments also imply less than desirable character traits. What is curious about these comments is the construction; the instructors begin with a “positive” mention, then proceed to negate the students in some way. Did the instructors feel that they needed to couch their negative sentiments toward the students? Unlike the negative comments discussed below, these comments begin with a qualification, as if to say the students can be criticized because something else, something nice, was proffered first.

Tall, average build. African hairdo. Black young woman in the Carnegie program.

Again, this exemplifies a very obvious distinction made between the Carnegie Scholars and other, nonminority, students in the program. This is also perhaps a nod to the faculty's resistance to this program.

Attractive, sincere girl, hard working, but with poor background in the white culture, but no one worked more, tried harder. Good character. I would bet on her, given a job within her capability.

What defines a “poor background in the white culture” and how deficient could this student be if she gained entry as a graduate student at this pwi? And, if this student is so hard-working, why is it suggested that she wouldn't be successful in any job, and not just one “within her capability”?

Keeps to her own group.

To what group is the instructor referring? Is the assumption that the Scholar only kept company with other Scholars or other black students? If this is the case, did the Scholars keep to themselves because they felt unwelcome or were cognizant of the negative attitudes from faculty? In relation to other comments and evidence, it does not seem extraordinary that the Scholars may have sought refuge or solace with members of their cohort.

Petite, quite attractive, dresses quite well. Seems to come from a good, cultured home. Pretty talkative, wants to be noticed, but rarely has any worthwhile contribution of intelligent questions. Average student.

This comment (the student comes from “good, cultured home”) is relevant to the erroneous assumption and expectation that minority students are culturally and socially deficient in some way (as stated in the initial grant application, recruitment materials, and application requirements). Was this surprising or unexpected to the instructor? And as a result, does that imply that the student is arrogant in some way because she “wants to be noticed”?

Nice looking, well groomed black. About average ability.

Large girl, pleasant manner, beautiful voice, self-confident and capable. At least an average student, superior to any of the other blacks in the class.

The same instructor made these comments about the same student on two different evaluation cards. While this student is pleasant, self-confident, and capable, she is still classified as average. If she is far superior to the other blacks in the class (a phrase which in itself promulgates an unnecessary, problematic, and divisive hierarchy among the minority students), then why is she still considered an average student?

In the above ambiguous comments, there are suggestions of laziness and arrogance, which would be considered negative attributes. Instead of these particular attributes, could these students have been confident or referred to by another term that holds less negative connotation? These questionable comments were thinly veiled digs at the Scholars' character that are couched in seemingly positive physical compliments. Additionally, comments such as “[he] looks out for himself,” “[she's] moody,” and “[she has] enough dramatic talent to fake her way through” seem harsh in a way that is less about the students being academically incapable, but more about the instructors having obvious disdain for these students.

One instructor, who interfaced with many of the Scholars, seemed to go out of his way to be positive about the Scholars in a way that was distinctive from the other faculty members. The comments, made by the same instructor but about different Scholars, could best be described as constructive. This instructor, second only to Dr. Crowley, seemed to have empathy toward the Scholars and have their best interest in mind, even when frustrated with their performance. These constructive comments included:

Attractive young person who continues to grow toward maturity. Somewhat insecure but has good ability. Needs to experience success—and will.

Hard working, pleasant. A borderline performance in [course name] but seems to have the potential to improve as he gains academic experience.

Her work has never been as good as it ought to be: she should be getting more A's with her interests, intelligence, and commitment. But she will do well on a job, and should be a strong asset to any employer. I see her as one of the graduates of this class who ought to be snapped up first by prospective employers.

One comment of particular note was:

A pleasant and attractive girl. I sense that the problem of adapting to the academic environment here has been greater than most people think; but she has the ability, I believe. Her thoughts and actions are rather directed inward, suggesting her best area being processing rather than public service in a library.

By specifically mentioning the “problem of adapting to the academic environment here” this instructor gives a rare acknowledgment to the overall academic and social atmosphere of the school that proved problematic for many of the Scholars. Perhaps if more faculty conceded to this point, and chose to mentor or otherwise support these students of color, their matriculation through the program could have been different. This comment emphasizes the real need for supportive faculty mentors when working with minority students in graduate programs.

As has been noted, the Carnegie Scholars did not enjoy unequivocal support from the gsls faculty. Some were positive in their assessments on the grade cards, many were noncommittal, making perfunctory comments like “this student performed below average in this course.” Of the faculty and instructors completing the grade cards, five faculty were consistent in their negative comments about the Carnegie Scholars, even to the point of contradicting their instructional colleagues. These comments include:

Here is a strongly political person, effective to the degree that one rather comes to doubt her sincerity. Her whole manner sometimes suggests the primary objective of “looking out for number one.”

Tall and thin, disadvantaged background. Serious and intent, but has to run hard to keep up with the bottom of the class.

There were two sets of comments worth discussion, notable because they clearly display the differences in perceptions held by the faculty about the Carnegie Scholars. Several faculty were dogged in their criticism of these students, even when many of their instructor colleagues viewed the Scholars through a very different lens. For example:

Pretty girl, but she just about balances her good looks with her poor personality. After each exam, I can “look forward to” a visit from ——, arguing and picking and grubbing for a higher grade. I have never tried this but I would expect that if I would offer —— a few free points on her exam just to get rid of her she would accept them. She just got married last summer; wonder what he's like?

Contrast this comment with one made by another instructor, about the same student:

A very quiet student in this course although I know her to be articulate and forceful outside of class. She appeared interested in some of the discussions and can probably contribute much more in a structured situation. She will make a competent librarian.

A second example:

Average student. Works hard to make passing grade. Fairly aggressive.

Again, contrast this comment with one made by another instructor, about the same student:

An excellent student. Very pleasant personality and appears to be a conscientious student.

Even with allowances for personality clashes and even disinterest in a particular subject or course, it seems unlikely that these students varied so wildly as to elicit such markedly different perceptions from members of the same faculty, without some attribution to the fact that some faculty members just didn't like, much less believe in, these minority Scholars.

Young, attractive and capable.

A very good person.

Very attractive, personable girl. Fine student.

Similar comments might even be considered somewhat neutral, implying that the instructor had little knowledge or interest in the student. However, even with disinterest, there were no negative comments or assumptions made about the students' personalities or character. For example:

About average for this course. I did not get to know her well enough to say much more. Quiet but effective. Does good work but lacks enthusiasm.

An attractive, friendly woman. Not really dynamic, but competent and intelligent.

Other comments were similarly brief, but positive and complimenting. These comments, which are scant on any mentions of physical appearance, include:

Enigmatic.

A very attractive and intelligent young woman who is a hard and conscientious worker.

Average in height, dark hair, wonderful personality and has all the promise of leadership in the school library field.

Mature, interesting person, who participated actively and intelligently in class discussions. Lively, pert, good person.

Along with the positive and complimenting comments were some comments that were unequivocally effusive with praise. For example:

She is attractive and immaculately groomed. She is a very good student. Her classroom contributions are intelligent and show considerable maturity of thought.

She is an asset to librarianship in every way. The library that gets her will be very fortunate. Her performance is tops; she is charming, gracious, always well groomed and dressed in the best of taste. She writes and speaks well.

Very bright, interested, conscientious. His written work is exceptionally good. My highest praise on all counts.

My best student. Attractive, alert, makes good comments to questions. Very Sharp. Should have unlimited future with her combination of brains and personality.

Finally, there were some comments about White gsls students that were less than positive. Not quite negative, these comments are interesting because they express real concerns with the students' performances, but they are constructed in such a way that the negative assertions are couched and seemingly explained away with assumptions that these students could and would do better, or that their performance could be justified by external factors. For example:

Although he appears to be somewhat distant and even condescending, I found that under the surface he was a mature and interested student; it is my guess that a basic shyness prevents him from making a better impression, at least on me. He is obviously intelligent and works hard, but there is some evidence he is carrying a very heavy load in order to support himself.

A pleasant girl with a strong sense of humor. Has the straightest B average I ever saw.

She seems to enjoy reference problems, does them adequately, but never brilliantly.

He failed to make a strong, positive impression, although he knows a lot. He's somewhat careless about personal appearance, but within reasonable bounds.

These comments could be perceived as simply giving less-than-stellar students the benefit of the doubt, they acknowledge extenuating life factors that many students encounter, and they exert some hope that they will do better in the future. Unfortunately, these same benefits and kindnesses were not typically extended to the students of color.

## Conclusions and Further Study

According to a news report local to the University of Illinois, the number of new African American students joining the University of Illinois in the fall of 2014 fell to 356, which was lower than the number of students in 1967, which spurred the Project 500 initiative.43 The last time 500 new African Americans arrived on campus was in 2008, and the number had been on the decline since then. And not since the Carnegie Scholars has gslis had as many students of color enrolled and matriculate at one time. It's interesting, but also disheartening, to see how history seems to be repeating itself.

In terms of the graduate programs in library and information science, there are other profession-wide issues at play in regards to minority recruitment and retention, and an overall declining number of professional lis positions. And at an even larger level, there are significant and ongoing issues of minority student retention in the higher education landscape.44 With this in mind, additional research should be done and more consideration should be paid to the retention and support of qualified and aspiring lis professionals of color. The exemplar of the Carnegie Scholars demonstrates that while financial support was crucial (the Scholars had all expenses paid for, and not just a nominal stipend), it was the in-house support of a dedicated program director that made the difference with this group of students. There are many successful lis recruitment programs that provide funding and opportunities for professional networking and mentoring, however, they typically do not have the mandate or ability to ensure that minority scholars have support within their programs.45 While there were definitely detractors that marred the experience of the Carnegie Scholars, there were also many who aided them in becoming successful lis professionals, none more so than Terry Crowley; he was their personal champion, advocate, and caregiver from the beginning to the successful conclusion of their programs.

In many instances this unique and dynamic group of students was not integrated into the general, White, student body of the lis program and they were made to feel academically and socially inferior—they were made to feel like guests in someone else's house.46 There needs to be a quality and substantive connection between students and their institution, in terms of both academic interest and personality, and there needs to be a match between students and the faculty with whom they are working and from whom they are learning. In the case of the Carnegie Scholars, Terry Crowley was that match. It is worth noting that Crowley joined the School's faculty in 1969 as the director of the Library Research Center, when he was thirty-four years old.47 For a young professional to shepherd this group of minority students and shield them from nonsupportive and possibly obstructive colleagues, all the while pushing back against the climate of the times, made Crowley's commitment to and advocacy for the Scholars even more significant and compelling. Crowley left the university in 1973 just before the last of the Scholars graduated; it can be speculated that his premature departure was due in part to the stress and difficulties he encountered as he guided the Scholars through the graduate program.48

The Carnegie Scholars Experiment is an important historical episode for the University of Illinois, and for lis as a whole. This program informs how we should look at not only recruitment, but also the retention, support, and care of students of color. Financial support is needed, but it is not the only factor to be considered. In addition to substantive financial assistance, lis programs will need dedicated and committed faculty, and multiple levels of in-house supports to replicate the Carnegie Experiment. Programs such as this will benefit individual schools and enrich the profession as a whole.

## NOTES

1.

Terence Crowley, “Minority Students at gslis: The Carnegie Experiment,” in Ideals and Standards: The History of the University of Illinois Graduate School of Library and Information Science, 1893–1993 (Urbana: University of Illinois, 1992), 223.

2.

Caroline Sotello Viernes Turner, “Guests in Someone Else's House: Students of Color,” Review of Higher Education 17, no. 4 (1994): 355–70.

3.

Lee A. Daniels, “Only the Appearance of Diversity: Higher Education and the Pluralist Ideal in the 1980's and 1990's,” Policy Perspectives, 1991 (Occasional papers published by the Pew Higher Education Research Program, Philadelphia), 355, 356.

4.

For higher education at large, see Vincent Tinto, Leaving College: Rethinking the Causes and Cures of Student Attrition (Chicago: University of Chicago Press, 1987); Mardy T. Eimers and Gary R. Pike, “Minority and Nonminority Adjustment to College: Differences or Similarities?” Research in Higher Education 38, no. 1 (1997): 77–97; Chalsa M. Loo and Garry Rolison, “Alienation of Ethnic Minority Students at a Predominantly White University,” Journal of Higher Education 57, no. 1 (January–February 1986): 58–77; Helen D. Just, Minority Retention in Predominantly White Universities and Colleges: The Importance of Creating a Good “Fit” (December 1999), St. Edwards University, Austin, TX, eric, ed 439 641. For library and information science see Kyung-Sun Kim and Sei-Ching Joanna Sin, “Recruiting and Retaining Students of Color in lis Programs: Perspectives of Library and Information Professionals,” Journal of Education for Library and Information Science 47, no. 2 (Spring 2006): 81–95; Teresa Y. Neely and Lorna Peterson, “Achieving Racial and Ethnic Diversity among Academic and Research Librarians. The Recruitment, Retention, and Advancement of Librarians of Color—A White Paper,” College and Research Libraries News 68, no. 9 (2007): 562–65; Cheryl Knott Malone, “Toward a Multicultural American Public Library History,” Libraries and Culture 35, no. 1 (Winter 2000): 77–87; Todd Honma, “Trippin' Over the Color Line: The Invisibility of Race in Library and Information Studies,” InterActions: ucla Journal of Education and Information Studies 1, no. 2 (2005), https://escholarship.org/uc/item/4nj0w1mp; Nicole A. Cooke, “The Spectrum Doctoral Fellowship Program: Enhancing the lis Professoriate,” InterActions: ucla Journal of Education and Information Studies 10, no. 1 (2014). https://escholarship.org/uc/item/7vb7v4p8; Rosemary Ruhig Du Mont, “Race in American Librarianship: Attitudes of the Library Profession,” Journal of Library History 21, no. 3 (Summer 1986): 488–509.

5.

Elizabeth Figa and Janet Macpherson, “Brown v. Board and Its Effect on Libraries and Library and Information Science Education,” in Unfinished Business: Race, Equity, and Diversity in Library and Information Science Education, ed. Maurice Wheeler (Lanham, MD: Scarecrow Press, 2005), 5.

6.

John R. Thelin, A History of American Higher Education (Baltimore: Johns Hopkins University Press, 2011); Elias Blake, “Is Higher Education Desegregation a Remedy for Segregation But Not Educational Inequality? A Study of the Ayers v. Mabus Desegregation Case,” Journal of Negro Education 60, no. 4 (1991): 538–65; Rosabeth Moss Kanter, “Some Effects of Proportions on Group Life: Skewed Sex Ratios And Responses to Token Women,” American Journal of Sociology (1977): 965–90.

7.

Figa and Macpherson, “Brown v. Board,” 16–19.

8.

Sylvia Hurtado, Jeffrey Milem, Alma Clayton-Pedersen, and Walter Allen, Enacting Diverse Learning Environments: Improving the Climate for Racial/Ethnic Diversity in Higher Education, ashe-eric Higher Education Report 26, no. 8 (Washington, DC: eric Clearinghouse on Higher Education, 1999), 9.

9.

Malone, “Toward a Multicultural American Public Library History,” 77–87; Cheryl Knott Malone, “Autonomy and Accommodation: Houston's Colored Carnegie Library, 1907–1922,” Libraries and Culture (1999): 95–112; Malone, “Louisville Free Public Library's Racially Segregated Branches, 1905–35,” Register of the Kentucky Historical Society 93, no. 2 (1995): 159–79; Klaus Musmann, “The Ugly Side of Librarianship: Segregation in Library Services from 1900 to 1950,” in Untold Stories: Civil Rights, Libraries, and Black Librarianship, ed. John Mark Tucker (Urbana: University of Illinois, 1998): 78–92; Wayne A Wiegand, “Tunnel Vision and Blind Spots: What the Past Tells Us about the Present; Reflections on the Twentieth-Century History of American Librarianship,” Library Quarterly (1999): 1–32; Wiegand. Part of Our Lives: A People's History of the American Public Library (Oxford: Oxford University Press, 2015).

10.

Rosemary Ruhig Du Mont, “The Educating of Black Librarians: An Historical Perspective,” Journal of Education for Library and Information Science (1986): 234.

11.

Du Mont, “Race in American Librarianship,” 491–92.

12.

Du Mont, “Educating of Black Librarians,” 233–49; Allison M. Sutton, “Bridging the Gap in Early Library Education History for African Americans: The Negro Teacher-Librarian Training Program (1936–1939),” Journal of Negro Education (2005): 138–50.

13.

Du Mont, “Educating of Black Librarians,” 242–43.

14.

Project 500 is the popular name of the initiative; its official title was The Special Education Opportunities Program.

15.

Joy Ann Williamson, Black Power on Campus: The University of Illinois, 1965–75 (Urbana: University of Illinois Press, 2003), 60.

16.

Ibid., 66.

17.

Ibid., 68; transcript, Oral History—Clarence Shelley, March 12, 2013, by Joshua T. Doil, University of Illinois Ethnography of the University Initiative, online: http://hdl.handle.net/2142/42623 (August 17, 2016); Paolo Cisneros, “How Project 500 Shaped Diversity on Campus,” The Daily Illini (Urbana, IL) October 22, 2008, http://www.dailyillini.com/article/2008/10/how-project-500-shaped-diversity-on-campus.

18.

Doil, Oral History.

19.

Cisneros, Project 500.

20.

Raven Hill, “Diversity Now,” Diverse Issues in Higher Education 25, no. 19 (2008): 11.

21.

Williamson, Black Power on Campus, 80.

22.

Jean J. Schensul, “Historical Context,” in The sage Encyclopedia of Qualitative Research Methods (Los Angeles: sage Publications, 2008), 392–93.

23.

K. Charmaz, Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis (London: Sage, 2006); Johnny Saldaña, The Coding Manual for Qualitative Researchers (London: Sage, 2015).

24.

Terence Crowley, grant proposal, “Program to Prepare Disadvantaged Students for a Career in Librarianship,” 1969, Box 1, Library Research Center Subject File, 1961–1975, Urbana, IL.

25.

Ibid., 6.

26.

Ibid.

27.

Crowley, “Minority Students at gslis,” 225.

28.

Crowley, “Program to Prepare Disadvantaged Students,” 2.

29.

Crowley, “Minority Students at gslis,” 225.

30.

Ibid., 227.

31.

Terence Crowley, grant proposal, “Proposal for an Institute for Training in Librarianship,” 1970, Box 1, 6–7, Library Research Center Subject File, 1961–1975, University of Illinois Archives, Urbana.

32.

Crowley, “Program to Prepare Disadvantaged Students,” 1.

33.

Crowley, “Proposal for an Institute,” 2.

34.

Crowley, “Program to Prepare Disadvantaged Students,” 2.

35.

The half semester presumably refers to the summer term that preceded the official beginning of the program in the fall semester.

36.

Crowley, “Program to Prepare Disadvantaged Students,” 2.

37.

Ibid., 3–4.

38.

Crowley, “Minority Students at gslis,” 223, 227.

39.

Crowley, “Program to Prepare Disadvantaged Students,” 6.

40.

Crowley, “Proposal for an Institute,” 3.

41.

Ibid., appendix.

42.

“Alumni File, 1893–1963,” Director's Office Subject File, Misc. boxes, Graduate School of Library and Information Science, University of Illinois Archives, Urbana.

43.

Christine Des Garennes, “Prescription for Disaster,” Champaign News- Gazette, October 12, 2014, http://www.news-gazette.com/news/local/2014-10-12/prescription-disaster.html.

44.

Tinto, Leaving College.

45.

The University of Arizona, School of Information's Knowledge River program is perhaps the only current diversity recruitment program that provides internal and external support in the way that the Carnegie Scholars program did. https://ischool.arizona.edu/knowledge-river-0.

46.

Turner, Guests in Someone Else's House, 355–70.

47.

“Terence Crowley Is Now Director of the Library Research Center, Graduate School of Library Science, University of Illinois,” Library Journal 94 (April 15, 1969): 1577.

48.

Miles W. Martin, “Community Information Specialist at the University of Toledo,” Reference Quarterly 12, no. 4 (Summer 1973): 361–63.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
# Is there a fundamental reason why in the Standard Model, there is no Feynman rule for a vertex with more than 4 legs?

In Standard Model, there are vertices with 3 legs (example: $$W l\nu$$) and with 4 legs (example: $$WWWW$$).

Is there a fundamental reason why in the Standard Model, there is no Feynman rule where a vertex has more than 4 legs?

Is this possible in Beyond-the-Standard-Model theories?

(You do have Feynman diagrams of higher dimension, like a 4-fermion diagram with exchange of a vector boson: four fermions are of dimension 6. So in the effective theory for that, it is summarized by 4-Fermi coupling with a dimensionful (-2) effective $$G_F$$.)

format: plain


text:
# [deep learning experiment] the second time: analysis and prediction of influencing factors of fiscal revenue

## Relevant knowledge

Set variable X ( 0 ) = { X ( 0 ) ( i ) , i = 1 , 2 , . . . , n } X^{(0)}=\{X^{(0)}(i),i=1,2,...,n\} X(0)={X(0)(i),i=1,2,...,n} is a nonnegative monotone original data sequence, and a grey prediction model is established: firstly X ( 0 ) X^{(0)} X is accumulated once X ( 1 ) = { X ( 1 ) ( K ) , k = 1 , 2 , . . . , n } X^{(1)}=\{X^{(1)}(K),k=1,2,...,n\} X(1)={X(1)(K),k=1,2,...,n}. yes X ( 1 ) X^{(1)} X(1) can establish the following first-order linear differential equation:

d X ( 1 ) d t + a X ( 1 ) = u \frac{dX^{(1)}}{d_t}+aX^{(1)}=u dtdX(1)+aX(1)=u

GM(1,1) model.

By solving the differential equation, the prediction model is as follows:

X ^ ( 1 ) ( k + 1 ) = [ X ^ ( 1 ) ( 0 ) − u ^ a ^ ] e − a ^ k + u ^ a ^ \hat{X}^{(1)}(k+1)=[\hat{X}^{(1)}(0)-\frac{\hat{u}}{\hat{a}}]e^{-\hat{a}k}+\frac{\hat{u}}{\hat{a}} X^(1)(k+1)=[X^(1)(0)−a^u^]e−a^k+a^u^

Since the GM(1,1) model obtains a one-time accumulation, the data obtained by GM(1,1) model X ^ ( 1 ) ( k + 1 ) \hat{X}^{(1)}(k+1) X^(1)(k+1) is reduced to X ^ ( 0 ) ( k + 1 ) \hat{X}^{(0)}(k+1) X^(0)(k+1), i.e X ( 0 ) X^{(0)} The grey prediction model of X(0) is:

X ^ ( 0 ) ( k + 1 ) = ( e − a ^ − 1 ) [ X ^ ( 0 ) ( n ) − u ^ a ^ ] e − a ^ k \hat{X}^{(0)}(k+1)=(e^{-\hat{a}}-1)[\hat{X}^{(0)}(n)-\frac{\hat{u}}{\hat{a}}]e^{-\hat{a}k} X^(0)(k+1)=(e−a^−1)[X^(0)(n)−a^u^]e−a^k

The following is the accuracy test table of posterior error model:

Extraction code: ygtl

Official documents show that the interface ttk of Tkinter and the new theme widget since Python 3.1 have been included in the python standard library. My experimental environment is Python 3.0 under Windows 6 (virtual environment under anaconda3), so the content in the original report [Ubuntu environment] is omitted

sudo apt-get update
sudo apt-get install python3-tk

## Data exploration and analysis

There are many factors affecting fiscal revenue (Y). Through the interpretation of fiscal revenue by economic theory and the observation of practice, we can get the following relevant factors: the number of social employees, the total wages of on-the-job employees, the total retail sales of social consumer goods, the disposable income of urban residents, the per capita consumption expenditure of urban residents, the investment in fixed assets of the whole society, the total population at the end of the year, the regional GDP Output value of primary industry, tax, consumer price index, output value ratio of tertiary industry to secondary industry, and consumption level of residents.

## Descriptive analysis

Open terminal, and the dependent packages required for this experiment are as follows:

pip install pandas==0.23.4
pip install xlrd==1.2.0
pip install xlwt==1.3.0
pip install keras==2.2.4
pip install tensorflow==1.3.0
pip install matplotlib==2.0.2

Firstly, the descriptive statistical analysis of the data is carried out to obtain the overall understanding of the data. The code is as follows, append it to gaikuo Py file.

import numpy as np
import pandas as pd

inputfile = 'finance/data1.csv'
r = [data.min(), data.max(), data.mean(), data.std()]
r = pd.DataFrame(r, index=['Min', 'Max', 'Mean', 'STD']).T
r = np.round(r, 2)
print(r)

The mean and standard deviation of fiscal revenue (y) obtained from the above are 618.08 and 609.25 respectively This shows that there are great differences in the fiscal revenue of a city in each year. After 2008, the fiscal revenue of the city in each year increased significantly.

## correlation analysis

The correlation coefficient can be used to describe the relationship between quantitative variables and explanatory variables, and preliminarily judge whether there is linear correlation between dependent variables and explanatory variables.

Create a new python file named correlation py

import numpy as np
import pandas as pd

inputfile = 'finance/data1.csv'
print(np.round(data.corr(method='pearson'), 2))

The operation results are as follows:

It can be seen from the above that the linear relationship between consumer price index (x11) and fiscal revenue is not significant, and there is a negative correlation. Other variables have a high positive correlation with fiscal revenue.

## model building

Fiscal revenue and forecast models of various types of revenue

### Prediction model of fiscal revenue in a city

A combined prediction model of grey prediction and neural network is established for the factors affecting fiscal revenue. Since Python and the extension library do not provide the function of grey prediction, the grey prediction function gm() is written to predict the local fiscal revenue. The factors used to predict the impact of fiscal revenue include the number of social employees (x1), the total wages of on-the-job employees (x2), the total retail sales of social consumption (x3), the per capita disposable income of urban residents (x4), the per capita consumption expenditure of urban residents (x5) and the investment in fixed assets (x7). The values of 2014 and 2015 are obtained through the gray prediction model established by python.

Create a new python file named 1-huise py

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

inputfile = 'finance/data1.csv'  # Input data file
outputfile = 'finance/data1_GM11.xls'  # Path saved after grey prediction
data.index = range(1994, 2014)
data.loc[2014] = None
data.loc[2015] = None

def gm(x0):  # Custom grey prediction function
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)  # Calculation parameters
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

l = ['x1', 'x2', 'x3', 'x4', 'x5', 'x7']
for i in l:
f = gm(data[i][:len(data) - 2].values)[0]
data[i][2014] = f(len(data) - 1)  # 2014 forecast results
data[i][2015] = f(len(data))  # 2015 forecast results
data[i] = data[i].round(2)  # Keep six decimal places
data[l + ['y']].to_excel(outputfile)  # Result output

A neural network prediction model is established for the factors affecting fiscal revenue. Its parameter setting is error accuracy 1 0 − 7 10^{-7} 10 − 7, 10000 learning times (6 nodes of input layer, 12 nodes of hidden layer and 1 node of output layer).

Create a new python file named: 1-yuce py

The neural network prediction code is as follows, which is added to 1-yuce Py file:

import pandas as pd

inputfile = 'finance/data1_GM11.xls'
outputfile = 'finance/revenue.xls'
modelfile = 'finance/1-net.model'
feature = ['x1', 'x2', 'x3', 'x4', 'x5', 'x7']
data_train = data.loc[range(1994, 2014)].copy()
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std
x_train = data_train[feature].as_matrix()
y_train = data_train['y'].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Activation

net_file = 'net.model'
net = Sequential()
net.fit(x_train, y_train, nb_epoch=10000, batch_size=16)  # Training model, learning 10000 times
net.save_weights(modelfile)  # Save model parameters
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data['y_pred'] = net.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
print(data)
import matplotlib.pyplot as plt

p = data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()

The operation results are as follows:

After standardizing the zero mean value of the data, it is brought into the three-layer neural network prediction model established by the local fiscal revenue (6 nodes in the input layer, 12 nodes in the hidden layer and 1 node in the output layer), and the predicted value of a city's fiscal revenue in 2015 is 253.315 billion yuan.

The above figure shows the comparison between the real value and the predicted value of local fiscal revenue. It can be seen from the figure that the overall trend of the real value and the predicted value is the same, but there are still errors.

### VAT forecasting model

The grey prediction model is established for the factors affecting value-added tax, and the posterior error ratio and prediction accuracy grade are obtained. Among them, the variables of the influencing factors of value-added tax include: total import value of goods (x1), industrial added value (x3) and the proportion of industrial added value in GDP (x5).

Create a new python file named: 2-huise py

The specific code of the VAT forecast model is as follows, which is added to 2-huise Py file:

import numpy as np
import pandas as pd

inputfile = 'finance/data2.csv'
outputfile = 'finance/data2_GM11.xls'
data.index = range(1999, 2014)
data.loc[2014] = None
data.loc[2015] = None

def gm(x0):  # Custom grey prediction function
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)  # Calculation parameters
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

l = ['x1', 'x3', 'x5']
for i in l:
f = gm(data[i][:len(data) - 2].as_matrix())[0]
data[i][2014] = f(len(data) - 1)
data[i][2015] = f(len(data))
data[i] = data[i].round(6)
data[l + ['y']].to_excel(outputfile)

The neural network prediction model (3 nodes in the input layer, 6 nodes in the hidden layer and 1 node in the output layer) is established for the factors affecting the value-added tax. The learning times are 10000 times and the error accuracy is high 1 0 − 7 10^{-7} 10−7.

Create a new python file named: 2-yuce py

The specific code of the prediction model is as follows, which is added to 2-yuce Py file:

import pandas as pd

inputfile = 'finance/data2_GM11.xls'
outputfile = 'finance/VAT.xls'
modelfile = 'finance/2-net.model'
feature = ['x1', 'x3', 'x5']

data_train = data.loc[range(1999, 2014)].copy()
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std
x_train = data_train[feature].as_matrix()
y_train = data_train['y'].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Activation

model = Sequential()
model.fit(x_train, y_train, nb_epoch=10000, batch_size=16)
model.save_weights(modelfile)
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data['y_pred'] = model.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
print(data)
import matplotlib.pyplot as plt

p = data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()

After standardizing the zero mean value of the data and substituting it into the three-layer neural network prediction model established by value-added tax, the predicted value of value-added tax in 2015 is 2685.403 million yuan. See the figure below for relevant data.

The comparison between the real value of VAT and the predicted value is shown below

Impact on business The grey prediction model is established based on the factors of tax, and the posterior error ratio and prediction accuracy grade are obtained. Among them, the variables affecting the value-added tax include: the total social investment in fixed assets (x3), the urban retail price index (x4), the loss of state-owned and state-controlled industries above Designated Size (x6) and the proportion of total profits of construction enterprises (x8).

Create a new python file named: 3-huise py

The specific code of business tax grey prediction model is as follows, which is added to 3-huise Py file

import numpy as np
import pandas as pd

inputfile = 'finance/data3.csv'
outputfile = 'finance/data3_GM11.xls'
data.index = range(1999, 2014)
data.loc[2014] = None
data.loc[2015] = None

def gm(x0):
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

l = ['x3', 'x4', 'x6', 'x8']
for i in l:
f = gm(data[i][:len(data) - 2].as_matrix())[0]
data[i][2014] = f(len(data) - 1)
data[i][2015] = f(len(data))
data[i] = data[i].round()
data[l + ['y']].to_excel(outputfile)
print(data[l + ['y']])

The neural network prediction model (4 nodes in the input layer, 8 nodes in the hidden layer and 1 node in the output layer) is established for the factors affecting the business tax. The learning times are 10000 times and the error accuracy is high 1 0 − 7 10^{-7} 10−7.

Create a new python file named: 3-yuce py

The specific code of business tax neural network prediction model is as follows, which is added to 3-yuce Py file

import pandas as pd

inputfile = 'finance/data3_GM11.xls'
outputfile = 'finance/sales_tax.xls'
modelfile = 'finance/3-net.model'
feature = ['x3', 'x4', 'x6', 'x8']
data_train = data.loc[range(1999, 2014)].copy()
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std
x_train = data_train[feature].as_matrix()
y_train = data_train['y'].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Activation

net = Sequential()
net.fit(x_train, y_train, nb_epoch=10000, batch_size=16)
net.save_weights(modelfile)
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data['y_pred'] = net.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
print(data)
import matplotlib.pyplot as plt

data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()

The operation results are as follows:

After standardizing the zero mean value of the data and substituting it into the three-layer neural network prediction model established by the business tax (4 nodes in the input layer, 8 nodes in the hidden layer and 1 node in the output layer), the predicted value of the business tax in 2015 is 22640.86 million yuan. The relevant data are shown in the figure.

The above figure shows the comparison between the real value of business tax and the predicted value. It can be seen that the overall trend of the real value and the predicted value is the same, but there is an error between the real value and the predicted value.

### Prediction model of enterprise income tax

A grey prediction model is established for the factors affecting enterprise income tax

The variables affecting the enterprise income tax (y) include the added value of the secondary industry (x1), the added value of the tertiary industry (x2), the total investment in fixed assets (x3), the retail price index of urban commodities (1978 = 100) (x4), the loss of state-owned and state-controlled Industrial Enterprises above Designated Size (x6), the total output value of the construction industry (x7), the retail sales of chain stores above Designated Size (x9) and the total local fiscal revenue (x10)

Create a new python file named: 4-huise py

The specific code of the grey prediction model of enterprise income tax is as follows, which is added to 4-huise In PY

import numpy as np
import pandas as pd

inputfile = 'finance/data4.csv'
outputfile = 'finance/data4_GM11.xls'
data.index = range(2002, 2014)
data.loc[2014] = None
data.loc[2015] = None

def gm(x0):
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

l = ['x1', 'x2', 'x3', 'x4', 'x6', 'x7', 'x9', 'x10']
for i in l:
f = gm(data[i][:len(data) - 2].as_matrix())[0]
data[i][2014] = f(len(data) - 1)
data[i][2015] = f(len(data))
data[i] = data[i].round(2)
data[l + ['y']].to_excel(outputfile)
print(data[l + ['y']])

Create a new python file named: 4-yuce py

At 4-yuce Write code in py to establish a neural network model of the factors affecting enterprise income tax

import pandas as pd
import numpy as np

inputfile = 'finance/data4_GM11.xls'
outputfile = 'finance/enterprise_income.xls'
modelfile = 'finance/4-net.model'
feature = ['x1', 'x2', 'x3', 'x4', 'x6', 'x7', 'x9', 'x10']
data_train = data.loc[range(2002, 2014)].copy()
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std
x_train = data_train[feature].as_matrix()
y_train = data_train['y'].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Activation

net = Sequential()
net.fit(x_train, y_train, nb_epoch=10000, batch_size=16)
net.save_weights(modelfile)
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data['y_pred'] = net.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
print(data)
import matplotlib.pyplot as plt

data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()

The operation results are as follows: the following are the historical data and predicted values of enterprise income tax and its related factors

The zero mean value of the data is standardized or substituted into the three-layer neural network prediction model established by the enterprise income tax (8 nodes in the input layer, 6 nodes in the hidden layer and 1 node in the output layer), and the predicted value of the enterprise income tax in 2015 is 17819.2 million yuan. The relevant data are shown in the figure above.

The comparison between the real value and the predicted value of enterprise income tax is shown below

There is a comparison chart between the real value and the predicted value. We can conclude that the overall trend of the real value and the predicted value is the same.

### Individual income tax prediction model

Create a new python file named: 5-huise py

At 5-huise Py file, which is used to establish a grey prediction model for the influencing factors of individual income tax

import numpy as np
import pandas as pd

inputfile = 'finance/data5.csv'
outputfile = 'finance/data5_GM11.xls'
data.index = range(2000, 2014)

data.loc[2014] = None
data.loc[2015] = None
l = ['x1', 'x4', 'x5', 'x7']

def gm(x0):  # Custom grey prediction function
import numpy as np
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)  # Calculation parameters
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

for i in l:
f = gm(data[i][:len(data) - 2].as_matrix())[0]
data[i][2014] = f(len(data) - 1)
data[i][2015] = f(len(data))
data[i] = data[i].round()

data[l + ['y']].to_excel(outputfile)

Create a new python file named: 5-yuce py

At 5-yuce The code written in py file is used to establish a neural network prediction model for the influencing factors affecting individual income tax, and its parameters are set to error accuracy 1 0 − 7 10^{-7} 10 − 7, 15000 learning times, 4 nodes of input layer, 8 nodes of hidden layer and 1 node of output layer.

The specific codes are as follows:

import pandas as pd
import numpy as np

inputfile = 'finance/data5_GM11.xls'
outputfile = 'finance/personal_Income.xls'
modelfile = 'finance/5-net.model'
feature = ['x1', 'x4', 'x5', 'x7']
data_train = data.loc[range(2000, 2014)].copy()
data_mean = data_train.mean()
data_std = data_train.std()
data_train = (data_train - data_mean) / data_std
x_train = data_train[feature].as_matrix()
y_train = data_train['y'].as_matrix()
from keras.models import Sequential
from keras.layers.core import Dense, Activation

net = Sequential()
net.fit(x_train, y_train, nb_epoch=15000, batch_size=16)
net.save_weights(modelfile)
x = ((data[feature] - data_mean[feature]) / data_std[feature]).as_matrix()
data['y_pred'] = net.predict(x) * data_std['y'] + data_mean['y']
data.to_excel(outputfile)
print(data)
import matplotlib.pyplot as plt

data[['y', 'y_pred']].plot(subplots=True, style=['b-o', 'r-*'])
plt.show()

The operation results are as follows:

The historical data of individual income tax and its related factors are predicted as follows:

After standardizing the zero mean value of the data and substituting it into the three-layer neural network prediction model established by the individual income tax (4 nodes in the input layer, 8 nodes in the hidden layer and 1 node in the output layer), the 2015 prediction value of the individual income tax is 648785.25. See the above figure for the relevant data.

It can be seen from the above figure that the overall trend of the real value of personal income tax is the same as that of the predicted value, and the error is relatively small.

### Revenue forecast model of government funds

Compared with previous years in 2006, the city's land transfer fee rose sharply in 2007, and the sharp rise in land transfer fee income directly affected the income of government funds. Therefore, in order to ensure the continuity of data, the grey prediction model is used to predict the income of government funds from 2007 to 2013

Create a new python file named: 6-yuce py

The specific code of the revenue prediction model of government funds is as follows, and it is added to 6-yuce Py file.

from __future__ import print_function
import numpy as np
import pandas as pd

def gm(x0):  # Custom grey prediction function
x1 = x0.cumsum()  # 1-AGO sequence
z1 = (x1[:len(x1) - 1] + x1[1:]) / 2.0  # Nearest neighbor MEAN (MEAN) generation sequence
z1 = z1.reshape((len(z1), 1))
B = np.append(-z1, np.ones_like(z1), axis=1)
Yn = x0[1:].reshape((len(x0) - 1, 1))
[[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Yn)  # Calculation parameters
f = lambda k: (x0[0] - b / a) * np.exp(-a * (k - 1)) - (x0[0] - b / a) * np.exp(-a * (k - 2))  # Restore value
delta = np.abs(x0 - np.array([f(i) for i in range(1, len(x0) + 1)]))
C = delta.std() / x0.std()
P = 1.0 * (np.abs(delta - delta.mean()) < 0.6745 * x0.std()).sum() / len(x0)
return f, a, b, x0[0], C, P

x0 = np.array([3152063, 2213050, 4050122, 5265142, 5556619, 4772843, 9463330])
f, a, b, x00, C, P = gm(x0)
print('2014 The forecast results in and 2015 are as follows:\n%0.2f Wan Yuanhe%0.2f Ten thousand yuan' % (f(8), f(9)))
print('The posterior error ratio is:%0.4f' % C)
p = pd.DataFrame(x0, columns=['y'], index=range(2007, 2014))
p.loc[2014] = None
p.loc[2015] = None
p['y_pred'] = [f(i) for i in range(1, 10)]
p.index = pd.to_datetime(p.index, format='%Y')
import matplotlib.pyplot as plt

p.plot(style=['b-o', 'r-*'], xticks=p.index)
plt.show()

The operation results are as follows:

The predicted results of 2014 and 2015 are 103870025600 yuan and 129297950700 yuan respectively.

The posterior error ratio of grey prediction is 0.2390, less than 0.35, and the prediction accuracy is good.

The comparison diagram between the real value and predicted value of grey prediction government funds is shown in the figure below

It can be seen from the above figure that the overall trend of the comparison between the real value of grey prediction government funds and the predicted value is the same, but due to the small amount of data, the fitting of the model is not very perfect and there are still great errors.

Posted by Alecdude on Fri, 13 May 2022 00:36:23 +0300


text:
# Bounded rationality

Last updated

Bounded rationality is the idea that rationality is limited, when individuals make decisions, by the tractability of the decision problem, the cognitive limitations of the mind, and the time available to make the decision. Decision-makers, in this view, act as satisficers, seeking a satisfactory solution rather than an optimal one.

## Contents

Herbert A. Simon proposed bounded rationality as an alternative basis for the mathematical modeling of decision-making, as used in economics, political science and related disciplines. It complements "rationality as optimization", which views decision-making as a fully rational process of finding an optimal choice given the information available. [1] Simon used the analogy of a pair of scissors, where one blade represents "cognitive limitations" of actual humans and the other the "structures of the environment", illustrating how minds compensate for limited resources by exploiting known structural regularity in the environment. [1] Many economics models assume that people are on average rational, and can in large enough quantities be approximated to act according to their preferences. With bounded rationality, Simon's goal was "to replace the global rationality of economic man with a kind of rational behavior that is compatible with the access to information and the computational capacities that are actually possessed by organisms, including man, in the kinds of environments in which such organisms exist." [2] In short, the concept of bounded rationality revises notions of "perfect" rationality to account for the fact that perfectly rational decisions are often not feasible in practice because of the intractability of natural decision problems and the finite computational resources available for making them.

The concept of bounded rationality continues to influence (and be debated in) different disciplines, including economics, psychology, law, political science and cognitive science. [3] Some models of human behavior in the social sciences assume that humans can be reasonably approximated or described as "rational" entities, as in rational choice theory or Downs Political Agency Models. [4]

## Origins

The term was coined by Herbert A. Simon. In Models of Man, Simon points out that most people are only partly rational, and are irrational in the remaining part of their actions. In another work, he states "boundedly rational agents experience limits in formulating and solving complex problems and in processing (receiving, storing, retrieving, transmitting) information". [5] Simon describes a number of dimensions along which "classical" models of rationality can be made somewhat more realistic, while sticking within the vein of fairly rigorous formalization. These include:

• limiting the types of utility functions
• recognizing the costs of gathering and processing information
• the possibility of having a "vector" or "multi-valued" utility function

Simon suggests that economic agents use heuristics to make decisions rather than a strict rigid rule of optimization. They do this because of the complexity of the situation.

## Model extensions

As decision-makers have to make decisions about how and when to decide, Ariel Rubinstein proposed to model bounded rationality by explicitly specifying decision-making procedures. [6] This puts the study of decision procedures on the research agenda.

Gerd Gigerenzer opines that decision theorists have not really adhered to Simon's original ideas. Rather, they have considered how decisions may be crippled by limitations to rationality, or have modeled how people might cope with their inability to optimize. Gigerenzer proposes and shows that simple heuristics often lead to better decisions than theoretically optimal procedures. [4]

Huw Dixon later argues that it may not be necessary to analyze in detail the process of reasoning underlying bounded rationality. [7] If we believe that agents will choose an action that gets them "close" to the optimum, then we can use the notion of epsilon-optimization, which means we choose our actions so that the payoff is within epsilon of the optimum. If we define the optimum (best possible) payoff as ${\displaystyle U^{*}}$, then the set of epsilon-optimizing options S(ε) can be defined as all those options s such that:

${\displaystyle U(s)\geq U^{*}-\epsilon }$.

The notion of strict rationality is then a special case (ε=0). The advantage of this approach is that it avoids having to specify in detail the process of reasoning, but rather simply assumes that whatever the process is, it is good enough to get near to the optimum.

From a computational point of view, decision procedures can be encoded in algorithms and heuristics. Edward Tsang argues that the effective rationality of an agent is determined by its computational intelligence. Everything else being equal, an agent that has better algorithms and heuristics could make "more rational" (more optimal) decisions than one that has poorer heuristics and algorithms. [8] Tshilidzi Marwala and Evan Hurwitz in their study on bounded rationality observed that advances in technology (e.g. computer processing power because of Moore's law, artificial intelligence and big data analytics) expand the bounds that define the feasible rationality space. Because of this expansion of the bounds of rationality, machine automated decision making makes markets more efficient. [9]

## Relationship to behavioral economics

Bounded rationality implies the idea that humans take reasoning shortcuts that may lead to suboptimal decision-making. Behavioral economists engage in mapping the decision shortcuts that agents use in order to help increase the effectiveness of human decision-making. One treatment of this idea comes from Cass Sunstein and Richard Thaler's Nudge . [10] [11] Sunstein and Thaler recommend that choice architectures are modified in light of human agents' bounded rationality. A widely cited proposal from Sunstein and Thaler urges that healthier food be placed at sight level in order to increase the likelihood that a person will opt for that choice instead of a less healthy option. Some critics of Nudge have lodged attacks that modifying choice architectures will lead to people becoming worse decision-makers. [12] [13]

Bounded rationality was shown to be essential to predict human sociability properties in a particular model by Vernon L. Smith and Michael J. Campbell. [14] There, an agent-based model correctly predicts that agents are averse to resentment and punishment, and that there is an asymmetry between gratitude/reward and resentment/punishment. The purely rational Nash equilibrium is shown to have no predictive power for that model, and the boundedly rational Gibbs equilibrium must be used to predict phenomena outlined in Humanomics. [15]

## Influence on social network structure

Recent research has shown that bounded rationality of individuals may influence the topology of the social networks that evolve among them. In particular, Kasthurirathna and Piraveenan [16] have shown that in socio-ecological systems, the drive towards improved rationality on average might be an evolutionary reason for the emergence of scale-free properties. They did this by simulating a number of strategic games on an initially random network with distributed bounded rationality, then re-wiring the network so that the network on average converged towards Nash equilibria, despite the bounded rationality of nodes. They observed that this re-wiring process results in scale-free networks. Since scale-free networks are ubiquitous in social systems, the link between bounded rationality distributions and social structure is an important one in explaining social phenomena.

## Notes

1. Gigerenzer, Gerd; Selten, Reinhard (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press. ISBN   978-0-262-57164-7.
2. Simon, Herbert A. (1955-02-01). "A Behavioral Model of Rational Choice". The Quarterly Journal of Economics. 69 (1): 99–118. doi:10.2307/1884852. ISSN   0033-5533. JSTOR   1884852.
3. Chater, Nick; Felin, Teppo; Funder, David C.; Gigerenzer, Gerd; Koenderink, Jan J.; Krueger, Joachim I.; Noble, Denis; Nordli, Samuel A.; Oaksford, Mike; Schwartz, Barry; Stanovich, Keith E. (2018-04-01). "Mind, rationality, and cognition: An interdisciplinary debate". Psychonomic Bulletin & Review. 25 (2): 793–826. doi:10.3758/s13423-017-1333-5. ISSN   1531-5320. PMC  . PMID   28744767.
4. Mancur Olson, Jr. ([1965] 1971). The Logic of Collective Action: Public Goods and the Theory of Groups, 2nd ed. Harvard University Press, Description, Table of Contents, and preview.
5. Oliver E. Williamson, p. 553, citing Simon.
6. Rubinstein, Ariel (1997). Modeling bounded rationality. MIT Press. ISBN   9780262681001.
7. Moss; Rae, eds. (1992). "Some Thoughts on Artificial Intelligence and Economic Theory". Artificial Intelligence and Economic Analysis. Edward Elgar. pp.  131–154. ISBN   978-1852786854.
8. Tsang, E.P.K. (2008). "Computational intelligence determines effective rationality". International Journal of Automation and Computing. 5 (1): 63–6. doi:10.1007/s11633-008-0063-6. S2CID   9769519.
9. Marwala, Tshilidzi; Hurwitz, Evan (2017). Artificial Intelligence and Economic Theory: Skynet in the Market. London: Springer. ISBN   978-3-319-66104-9.
10. Thaler, Richard H., Sunstein, Cass R. (April 8, 2008). Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press. ISBN   978-0-14-311526-7. OCLC   791403664.CS1 maint: uses authors parameter (link)
11. Thaler, Richard H., Sunstein, Cass R. and Balz, John P. (April 2, 2010). "Choice Architecture". doi:10.2139/ssrn.1583509. S2CID   219382170. SSRN  .Cite journal requires |journal= (help)CS1 maint: uses authors parameter (link)
12. Wright, Joshua; Ginsberg, Douglas (February 16, 2012). "Free to Err?: Behavioral Law and Economics and its Implications for Liberty". Library of Law & Liberty.
13. Sunstein, Cass (2009-05-13). Going to extreems: How Like Minds Unite and Divide. ISBN   9780199793143.
14. Michael J. Campbell; Vernon L. Smith (2020). "An elementary humanomics approach to boundedly rational quadratic models". Physica A. doi:10.1016/j.physa.2020.125309.
15. Vernon L. Smith and Bart J. Wilson (2019). Humanomics: Moral Sentiments and the Wealth of Nations for the Twenty-First Century. Cambridge University Press. doi:10.1017/9781108185561.
16. Kasthurirathna, Dharshana; Piraveenan, Mahendra (2015-06-11). "Emergence of scale-free characteristics in socio-ecological systems with bounded rationality". Scientific Reports. 5 (1): 10448. doi:. ISSN   2045-2322. PMC  . PMID   26065713.


text:
Dataset Open Access

# Pilot study: Ranking of textual snippets based on the writing style

Andi Rexha; Mark Kröll; Hermann Ziak; Roman Kern

### Citation Style Language JSON Export

{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.437461",
"title": "Pilot study: Ranking of textual snippets based on the writing style",
"issued": {
"date-parts": [
[
2017,
3,
22
]
]
},
"abstract": "
In this pilot study, we tried to capture humans' behavior when identifying authorship of text snippets. At first, we selected textual snippets from the introduction of scientific articles written by single authors. Later, we presented to the evaluators a source and four target snippets, and then, ask them to rank the target snippets from the most to the least similar from the writing style.
\n\n
The dataset is composed by 66 experiments manually checked for not having any clear hint during the ranking for the evaluators. For each experiment, we\u00a0have evaluations from three different evaluators.
\n\n
We present each experiment in a single line (in the CSV\u00a0file), where, at first we present the metadata of the Source-Article (Journal, Title, Authorship, Snippet), and the metadata for the 4 target snippets (Journal, Title, Authorship, Snippet, Written From the same Author, Published in the same Journal) and the ranking given by each evaluator. This task was performed in the open source platform, Crowd Flower.\u00a0
\n\n
The headers of the CSV are self-explained. In the TXT file, you can find a human-readable\u00a0version of the experiment.\u00a0
\n\n
For more information about the extraction of the data, please consider reading our paper: \"Extending Scientific Literature Search by Including the Author\u2019s Writing Style\" @BIR:\u00a0http://www.gesis.org/en/services/events/events-archive/conferences/ecir-workshops/ecir-workshop-2017\u00a0
",
"author": [
{
"family": "Andi Rexha"
},
{
"family": "Mark Kr\u00f6ll"
},
{
"family": "Hermann Ziak"
},
{
"family": "Roman Kern"
}
],
"note": "Acknowledgements:\nThe Know-Center is funded within the Austrian COMET Program under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labour and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.",
"type": "dataset",
"id": "437461"
}
734
225
views


text:
## Monday, March 28, 2005 ... //

### A new earthquake

Less than an hour ago, namely at 11:09:37 Eastern U.S. time, a new earthquake of magnitude 8.7 (originally reported as 8.2 or 8.5) occured on the same fault line as the December 2004 earthquake of magnitude 9.0 that caused the deadly tsunami. It was centered near Sumatra, an island that belongs to Indonesia - a few hundreds of kilometers South from the place of the previous earthquake. Thai officials warned that tsunami could follow.

Incidentally, the international help for the victims of that tsunami has exceeded 10 billion USD. Twenty percent of this amount comes from the U.S. - much like the 20 percent of the global GDP. The Reference Frame hopes and predicts that the smaller magnitude of the earthquake today - and smaller depth in which the soliton is supposed to be created (it's easier to create the soliton in deep ocean) - will translate into much lower casualties...

200 minutes after the earthquake, only 50 casualties are reported.


text:
### Session H5: Focus Session: Interfaces in Complex Oxides - Polar Interfaces and Ferroelectrics

8:00 AM–11:00 AM, Tuesday, February 28, 2012
Room: 206A

Chair: Susanne Stemmer, University of California, Santa Barbara

Abstract ID: BAPS.2012.MAR.H5.7

### Abstract: H5.00007 : Memristive behavior of ferroelectric tunnel junctions

9:12 AM–9:48 AM

Preview Abstract MathJax On | Off   Abstract

#### Author:

Alexei Gruverman
Employment of polarization reversal in ultrathin ferroelectric layers opens new possibilities for development of electronic devices with novel functional properties not available in conventional systems. A particularly promising aspect is realization of resistive switching in the ferroelectric tunnel junctions (FTJ), which can be used as non-charge based logical switches in nonvolatile memory devices. Functionality of FTJs is intrinsically linked to a relationship between polarization orientation and tunneling resistance, which brings about a problem of ferroelectric switching and polarization retention in ultrathin ferroelectric barriers. Here, we demonstrate a giant resistive switching effect of more than 105\% at room temperature in the FTJ device structure composed of an epitaxial BaTiO$_3$ ferroelectric barrier sandwiched between bottom and top electrodes. We provide experimental evidence of the memristive behavior of these FTJs where both the low- and high-resistance states can be tuned by the external voltage by several orders of magnitude. The mechanism of the memristive behavior in our junctions is discussed in terms of the modifications of the tunneling potential profile driven by the charge accumulation in the BaTiO$_3$ layer.