each rule’s observed performance. Therefore, from rule l’s back-tested return, which was 7.31 percent, we would subtract 7.31 percent,giving a result of zero. The result properly reflects rule 1’s lack of predic-tive power. From rule 2’s return of 1.78 percent, we would subtract a valueof 1.78 percent, also giving a value of zero, also revealing its lack of pre-dictive power.
The bottom line is this: by adjusting the back-tested (observed) per-formance by the expected return of a rule with no predictive power hav-ing an equivalent position bias, the deceptive component of performancecan be removed. In other words, one can define the benchmark for anyrule as the expected return of a nonpredictive rule with an equivalent po-sition bias.
A Simpler Solution to Benchmarking:Detrending the Market Data
The procedure just described can be quite burdensome when many rulesare being tested. It would require that a separate benchmark be computedfor each rule based on its particular position bias. Fortunately there is aneasier way.
The easier method merely requires that the historical data for themarket being traded (e.g., S&P 500 Index) be detrended prior to rule test-ing. It is important to point out that the detrended data is used only for thepurpose of calculating daily rule returns. It is not used for signal genera-tion if the time series of the market being traded is also being used as arule input series. Signals would be generated from actual market data (notdetrended).28 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
Detrending is a simple transformation, which results in a new marketdata series whose average daily price change is equal to zero. As pointedout earlier, if the market being traded has a net zero trend during the back-test period, a rule’s position bias will have no distorting effect on perfor-mance. Thus, the expected return of a rule with no predictive power, thebenchmark, will be zero if its returns are computed from detrended mar-ket data. Consequently, the expected return of a rule that does have pre-dictive power will be greater than zero when its returns are computedfrom detrended data.
To perform the detrending transformation, one first determines theaverage daily price change of the market being traded over the historicaltest period. This average value is then subtracted from each day’s pricechange.
The mathematical equivalence between the two methods discussed,(1) detrending the market data and (2) subtracting a benchmark with aequivalent position bias, may not be immediately obvious. A detailedmathematical proof is given in the Appendix, but if you think about it, youwill see that if the market's average daily price change during the histori-cal testing period is equal to zero, then rules devoid of predictive powermust have an expected return of zero, regardless of their long/short posi-tion bias.
To illustrate this point, let’s return to the formula for computing the ex-pected value of a random variable. You will notice that if the average dailyprice change of the market being traded is zero, it does not matter whatp(long) or p(short) are. The expected return (ER) will always be zero.
ER = [p (long) x avg. daily return] - [p (short) x avg. daily return]
For example, if the position biases were 60 percent long and 40 per-cent short, the expected return is zero.
0 = [0.60) x 0] - [0.40 x 0] Position Bias: 60 percent long, 40 percent short
If, on the other hand, a rule does have predictive power, its expectedreturn on detrended data will be greater than zero. This positive return re-flects the fact that the rule’s long and short positions are intelligent ratherthan random.
Using Logs of Daily Price Ratio Insteadof Percentages
Thus far, the returns for rules and the market being traded have been dis-cussed in percentage terms. This was done for ease of explanation. How-Objective Rules and Their Evaluation 29
ever, there are problems with computing returns as percentages. Theseproblems can be eliminated by computing daily returns as the logs of dailyprice ratios which is defined as:
current day's priceprior day's price
The log-based market returns are detrended in exactly the same wayas the percentage changes. The log of the daily price ratio for the marketbeing traded is computed for each day over the back-test period. The aver-age is found, and then this average is deducted from each day. This elimi-nates any trend in the market data.
OTHER DETAILS: THE LOOK-AHEAD BIAS ANDTRADING COSTS
 
It is said the devil lives in the details. When it comes to testing rules, thistruth applies. There are two more items that must be considered to ensureaccurate historical testing. They are (1) the look-ahead bias and the re-lated issue, assumed execution prices, and (2) trading costs.
Look-Ahead Bias and Assumed Execution Prices
Look-ahead bias,'! also known as “leakage of future information,” occursin the context of historical testing when information that was not trulyavailable at a given point in time was assumed to be known. In otherwords, the information that would be required to generate a signal wasnot truly available at the time the signal was assumed to occur.
In many instances, this problem can be subtle. If unrecognized, itcan seriously overstate the performance of rule tests. For example, sup-pose a rule uses the market’s closing price or any input series that onlybecomes known at the time of the close. When this is the case, it wouldnot be legitimate to assume that one could enter or exit a position at themarket’s closing price. Assuming this would infect the results withlook-ahead bias. In fact, the earliest opportunity to enter or exit wouldbe the following day’s opening price (assuming daily frequency informa-tion). All of the rules tested in Part Two of this book are based on mar-ket data that is known as of the close of each trading day. Therefore,the rule tests assume execution at the opening price on the followingday. This means that a rule’s daily return for the current day (day,) isequal to the rule’s output value (+1 or -1) as of the close of day, multi-3O METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
plied by the market’s change from the opening price of the next day(open day,,) price to the opening price on day after that (open day ,,).That price change is given as the log of the ratio defined as openingprice of day,, divided by the opening price on day, ,, as shown in the fol-lowing equation:
+2
Pos, x Log 0+1
Where:POS, = Rule’s market position as of the close of dayyO,, = Open S&P 500 on day,,O,, = Open S&P 500 on day,»
This equation does not show the detrended version of rule returns, asshown here:
p O,2OS, x Log >. — ALR
+1
Where:POS, = Rule’s market position as of the close of dayO,, = Open S&P 500 on day,,O,, = Open S&P 500 on day,,ALR = Average Log Return over Back Test
Look-ahead bias can also infect back-test results when a rule uses aninput data series that is reported with a lag or that is subject to revision.For example, the back-test of a rule that uses mutual fund cash statistics,!>which is released to the public with a two-week delay, must take this laginto account by lagging signals to reflect the true availability of the data.None of the rules tested in this book use information reported with a lagor that is subject to revision.Objective Rules and Their Evaluation 31
Trading Costs
Should trading costs be taken into account in rule back-tests? If the intentis to use the rule on a stand-alone basis for trading, the answer is clearlyyes. For example, rules that signal reversals frequently will incur highertrading costs than rules that signal less frequently and this must be takeninto account when comparing their performances. Trading costs includebroker commissions and slippage. Slippage is due to the bid-asked spreadand the amount that the investor's order pushes the market’s price—upwhen buying or down when selling.
If, however, the purpose of rule testing is to discover signals that con-tain predictive information, then trading costs can obscure the value of arule that reverses frequently. Since the intent of the rule studies con-ducted in this book are aimed at finding rules that have predictive powerrather than finding rules that can be used as stand-alone trading strategiesit was decided not to impose trading costs. 
The IllusoryValidity ofSubjectiveTechnical
Analysis
 
The difference between a crank and a charlatan isthe charlatan knows he is dealing in snake oil, thecrank does not.
—Martin Gardner
titude of skepticism toward subjective TA, a body of propositions
that are untestable because they lack cognitive content. Second, itunderscores the need for a rigorous and objective approach to knowledgeacquisition, to combat the human tendency to form and maintain strongbeliefs in the absence of solid evidence or even in the face of contradic-tory evidence.
Besides what we take on faith, most of us are under the impressionthat our beliefs are justified by sound reasoning from good evidence. Itcan be said that we know something when we have a belief that is trueand we hold it because we have drawn a correct inference from the rightevidence.' We know that ice cream is cold, gravity is real, and some dogsbite, on the basis of first-hand experience, but without the time or exper-tise to acquire all requisite knowledge directly, we willingly accept wis-dom from secondhand sources we deem reliable. However we come by it,we do not adopt knowledge willy-nilly, or so we believe.
Unfortunately, this belief and many others that we hold are erroneous.Without realizing it, by a process that is as automatic as breathing, weadopt all sorts of beliefs without rational thought or reliable evidence. Ac-cording to a growing body of research, this is due to a variety of cognitiveerrors, biases, and illusions. This is a serious liability, because once a
Te chapter has two purposes. First, it is intended to encourage an at-
3334 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
—_
B
a
AFIGURE 2.1 Senses can deceive us.
falsehood is adopted, it tends to persist even when new evidence shows itto be wrong. This longevity is also attributable to various cognitive errorsthat lead us to defend existing beliefs.
A visual illusion is an example of an erroneous belief that persistseven after it has been pointed out. In Figure 2.1, line segment A appearslonger than segment B, but if you apply a ruler, you will see they are ofequal length. However, this knowledge does not undo the illusion. Seeingcan be deceiving, and the deception lasts.
Figure 2.2? depicts another visual illusion. The table top on the rightappears more elongated. If you compare them you will see they have thesame dimensions.
Under normal circumstances, sensory impressions as interpreted bythe brain produce accurate beliefs about the world. The selective pres-sures of evolution assured the development of such a vital capability.However, as well adapted as the eye/brain system is, it is not perfect. Un-der adverse conditions, conditions outside of those that shaped its evolu-tion, the system can be fooled.
Just as there are illusory perceptions, there is illusory knowledge. Itseems valid, but it is not, and similar to perceptual illusions, false knowl-edge tends to occur in situations beyond those that shaped the evolution
FIGURE 2.2 Both table tops have same size and shape.“Turning the Tables” from MIND SIGHTS by Roger N. Shepard. Copyright © 1990by Roger N. Shepard. Reprinted by permission of Henry Holt and Company, LLC.The Illusory Validity of Subjective Technical Analysis 35
of our cognitive abilities. Under such adverse conditions, our generallysuccessful learning strategies fail, and we come to “know” what is not so.’
Over the last 30 years, cognitive psychologists have discovered thaterroneous knowledge is often the result of systematic errors (biases) inthe way we process information about complex and uncertain situa-tions. Financial market behavior is complex and uncertain, so it shouldbe no surprise that informal (subjective) analysis would produce illu-sory knowledge.
A systematic error, unlike a random error, occurs over and over againin similar situations. This is good news because it means the error is pre-dictable and steps can be taken to avoid it. The first step is realizing thatsuch errors are common.
SUBJECTIVE TA IS NOT LEGITIMATE KNOWLEDGE
 
As defined in Chapter 1, subjective TA is any method of analysis that, be-cause of vagueness, cannot be defined as an algorithm that can be backtested by computer. Though the subjective domain of TA is comprised ofmany different methods, they share a faith that valid knowledge about fi-nancial markets can be discovered in an informal and nonscientific man-ner. I contend this faith is misplaced.
As discussed in the Introduction, subjective TA cannot be calledwrong, because to call a method wrong implies it has been tested and con-tradicted by objective evidence. Subjective TA is immune to empiricalchallenge because it is untestable. Thus, it is worse than wrong; it is mean-ingless.
Beliefs that cannot be tested must be accepted on faith, anecdotal ev-idence, or authoritarian pronouncement . In this way, subjective TA is sim-ilar to practices such as New-Age medicine, astrology, numerology, and ahost of other untestable claims, none of which can legitimately be classi-fied as knowledge.
However, many TA practitioners and consumers believe strongly inthe validity of subjective methods, and if asked, no doubt each would as-sert that their belief is justified and supported by evidence. I contend thisis a consequence of the same cognitive flaws that underlie erroneous be-liefs in general and the unscientific manner in which traditional TA ispracticed.
TA is certainly not the only field to suffer the ill effects of nonscien-tific methods. Medicine and psychology, where the penalties for faultywisdom are much greater, are also burdened with erroneous knowledge.This has spawned an emerging movement called evidence-based medicine3@ ~~ METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
that appeals to doctors to restrict themselves to practices with proven ef-ficacy. There is a similar movement in psychotherapy. A journal called theScientific Review of Mental Health Practice’ describes its mission as rid-ding the field of unsubstantiated and untested treatments. Unfortunately,it is unlikely that traditional doctors and psychotherapists will abandontheir ingrained ways. Old beliefs are hard to change. However, new practi-tioners may be persuaded, and it is their patients who will benefit frommethods with proven efficacy.
Although this chapter is a critique of subjective TA, objective TA isalso vulnerable to erroneous knowledge. However, it comes about in anentirely different manner. Whereas subjective TA suffers from a lack ofquantitative evidence, objective TA suffers from faulty inferences drawnfrom quantitative evidence. This problem and possible solutions are ex-plored in Chapter 6.
A PERSONAL ANECDOTE: FIRST A TRUE TA BELIEVER,THEN A SKEPTIC
 
A book on the necessity of quantitative objective evidence may be astrange place for personal anecdotes. Nevertheless, I offer a firsthand ac-count of my initial conversion to a true believer in TA, my fall from faith,and my rebirth as a scientific skeptic who believes TA can offer valuewhen researched in a disciplined manner.
I first learned of TA as a teenager and wanted to believe its claimswere true. Making money from chart patterns seemed magical yetplausible. Until that time, my earnings were derived from summersspent tending lawns, digging clams on Long Island’s North Shore, andworking on a garbage truck at a local resort. Though I’ve never shiedaway from physical labor, the idea of making money without sweathad great appeal.
Like most TA true believers, my initial convictions came secondhand,reading the revealed wisdom of the field’s self-proclaimed authorities. Itnever occurred to me that their claims might not be grounded in sound re-search. The very term technical analysis had a scientific ring to it. Later itbecame clear that in many instances the authorities were not basing theirclaims on firsthand research but merely regurgitating what they had readfrom even earlier self-proclaimed experts. Humorist and philosopherArtemus Ward has said, “It’s not so much the things we don’t know thatget us into trouble as the things we know that just ain’t so.”
My first read was How I Made Two Million Dollars in the Stock Mar-ket by Nicholas Darvas, a professional dancer. He attributed his success toThe Illusory Validity of Subjective Technical Analysis 37
a charting method called the Box Theory. As my first exposure to TA, theidea that buy-and-sell signals could be derived from a stock’s behaviorwas entirely novel and exciting. Darvas based his analysis on a staircase-like arrangement of price ranges that he called boxes. Soon I learned thiswas old wine in a new bottle—the good old-fashioned support and resis-tance zones of TA. My enthusiasm was undeterred. Next I studied Chart-craft’s Point and Figure Method and began keeping charts on a largestable of stocks. For my sixteenth birthday my parents gave me the bible,Technical Analysis of Stock Trends, by Edwards and Magee, and my con-version was complete.
Before long, I was giving stock tips to my chemistry teacher, Mr. Corn.My initial success with a stock called Cubic made several other teachersfans of my mysterious charts. I picked several more winners. No one, noteven I, considered the possibility that these early successes might havebeen nothing more than a run of good luck. There was no reason to. Noneof the TA books I studied ever discussed the roles of randomness in mar-ket behavior or luck in the performance of an investment method. Myearly scores were plausible because they were consistent with the anec-dotes that filled the books, but it did not last. My track record began toblemish and my fans faded. However, my enthusiasm for TA continued togrow because it was always possible to explain away my failures. Afterthe fact, I could always see where I had misread the chart. I’d get it rightnext time.
In high school I was a good science student, although I now realize Iwas scientifically illiterate. Later, college courses in the philosophy of sci-ence taught me that science is more than a set of facts. First and foremost,it is a method for distinguishing real from illusory knowledge. It wouldtake me the better part of 40 years to see the connection between the phi-losophy of science and TA.
My skepticism grew out of my experience as a proprietary or “prop”trader for the firm of Spear, Leeds & Kellogg from the fall of 1996through the spring of 2002. Prop traders have the ultimate fun job. Theyare given the freedom to speculate with a firm’s capital. My trading strat-egy was based on what I had learned about TA over the prior 35 years. Imade profits during the first three and one-half years, October 1996through February 2000. Because my trading methods were subjective, itis not clear if these gains were the result of my abilities with TA or myever-bullish bias coinciding with an upward-trending market. I suspect itwas the latter because an analysis of my monthly returns relative to amarket benchmark indicated I was not beating the market but merelymatching it. In other words, I had not generated a significantly positivealpha. Also suggestive was the fact that I gave back all my gains in thetwo years after the market trend turned down in March 2000. Over theBB METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
full five-and-one-half-year period, my results, to put it generously, werelackluster.
Prior to joining Spear, Leeds & Kellogg, I had been a proponent ofobjective trading methods, so while at Spear, I made efforts to developa systematic trading program in hopes that it would improve my perfor-mance. However, with limited time and development capital, theseplans never came to fruition. Thus, I continued to rely on classical bar-chart analysis, supplemented with several indicators that I interpretedsubjectively.
However, I was objective in several ways. Early in my trading careerwith Spear, I began keeping a detailed journal. Prior to each trade I wouldnote its TA rationale. In addition, each trade was based on an objectivelyfalsifiable prediction—a defined point of adversity where I would admitthe trade was wrong. My managers insisted on this. I maintained the jour-nal daily over the five-plus years of trading. After each transaction wascompleted, I did a postmortem analysis. This practice made it hard for meto rationalize my failures. There they were staring me in the face. This ac-celerated my fall from faith in subjective TA.
Because my results were for only one person, I considered the possi-bility I was not properly implementing the dictates of the TA texts I hadbeen studying for the past 30+ years. Yet, I also began to wonder if whatthey said was correct or if what they said was even substantive.
When I discussed my growing skepticism with colleagues, their re-sponse was that TA was obviously valid. Trends and patterns in historicalcharts were simply too evident to be illusory. A widely used TA text’ as-serts that TA is valid on these grounds. This did not satisfy me. When Ilearned that the same patterns and trends,’ to which TA attributes suchsignificance, also appear with regularity in purely random data, my faith inchart analysis was shaken to the core. Moreover, it came to my attentionthat studies have shown that expert chart readers cannot reliably distin-guish actual market charts from charts produced by a random process.*Such charts, which are generated by random draws from a sample of ac-tual price changes, are, by design, devoid of authentic trends and patternsand, therefore, impossible to predict, but, to experienced chartists, theylooked just like authentic price charts. On this basis, it would seem thatpredictions based on authentic charts could not be trusted. Clearly, “obvi-ous validity” is an inadequate standard for judging the validity of marketpatterns.
With inspiration from two books, How We Know What Isn’t So byThomas Gilovich,’ and Why People Believe Weird Things by Michael Sher-mer,'° I came to the realization that TA must be approached with the skep-ticism and rigor of the scientific method.The Illusory Validity of Subjective Technical Analysis 39
THE MIND: A NATURAL PATTERN FINDER
 
We are predisposed to look for and find predictive patterns. Human natureis repulsed by the unpredictable and the unexplained, so our cognitivemachinery tends to perceive order, pattern, and meaning in the sensorystimuli we receive from the world regardless of whether it truly is or-dered, patterned, or meaningful. “In many instances, what we experienceis nothing more than the vagaries of chance at work.”!! “The appearanceof a face on the surface of the moon, the perception of Satanic messageswhen rock music is played backwards, or seeing the face of Jesus in thewood grain of a hospital door are examples of the mind imposing order onrandom visual sensory stimuli.””
The tendency to perceive order evolved because this ability was cru-cial for our survival.’ The early humans who were best at it produced themost offspring, and we are descended from them. Unfortunately, evolu-tion did not endow us with an equal ability to distinguish valid from in-valid patterns. As a consequence, along with the valid knowledge camemany falsehoods.
Acquiring knowledge, whether in prehistoric times or today, can failin two ways: learning a falsehood or failing to learn a truth. Of these twomistakes, we seem to be more prone to adopting falsehoods than we areto overlooking vital truths. Evolutionary biologists speculate that the ac-quisition of false ideas was less detrimental to the survival of early hu-mans than failing to learn something vital. The belief that a ritual danceprior to a hunt would promote success is a learned falsehood with mini-mal cost—a little wasted dancing—but the failure to learn the importanceof standing downwind of an animal during a hunt was an error with a sig-nificant survival cost.
As a result, our brains evolved to have a voracious, although indis-criminate, appetite for predictive patterns and causal relations. The falsebeliefs that accumulated along the way were a price worth paying. Some-times, a successful hunt did follow a ritual dance, so the superstitiouspractice was reinforced. Also, performing a ritual reduced anxiety by giv-ing an illusory sense of control over outcomes that were largely a matterof chance.
Modern civilized life has changed this picture considerably. Not onlyare decisions more complex today, but the costs of learned falsehoods aregreater. Consider the debate on global warming. Are rising temperaturesindicative of a real long-term danger or are they a false alarm? If thoseconvinced that it is a false alarm are later proven wrong, future genera-tions will pay dearly. However, the cost of treating global warming as a se-rious threat would be more modest if that view turns out to be mistaken.40 = METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
The evolution of human intelligence has been a slow process. Our in-tellectual capacities developed over a several-million-year period, most ofwhich took place under conditions referred to as the ancestral environ-ment of adaptation. In this environment, the mandates were few andclear: Survive and reproduce. Human intellectual capacities and thinkingstrategies were adapted for those conditions, not for the complexities ofmodern civilization, which date back a mere 10 to 15 thousand years. Inother words, 99 percent of human evolution took place in an environmentdramatically less complex than the one we face today. It is not surprising,then, that our intelligence would be maladapted to the complex judgmentand decision-making tasks we face today. We seem to be as superstitioustoday as the cave dweller who believed in ritual dances.
THE EPIDEMIC OF WEIRD BELIEFS
 
A voracious but indiscriminate appetite for knowledge inevitably leads toweird beliefs. Our susceptibility to the adoption of false beliefs was evi-denced dramatically in a 1990 Gallup survey of 1,236 adult Americans. Theproportion of people believing in all manner of nonsense including theparanormal is frightening.'* These are statistics presented by Shermer."
Astrology 52 percent
Extrasensory perception 46 percent
Witches 19 percent
Aliens from space landing on earth 22 percent
A previously existing continent called Atlantis 33 percentHumans and dinosaurs living at the same time 41 percentCommunicating with the spirits of the dead 42 percentGhosts 35 percent
Personal psychic experiences 67 percent
Astronomer Carl Sagan'® lamented that more people believe in astrol-ogy than the theory of evolution. He attributed this widespread irrational-ity and superstition to a high rate of scientific illiteracy, which surveysestimate to be over 95 percent. “In such an atmosphere, pseudo sciencethrives. Strange beliefs, such as New-Age health practices, are supportedin ways that purport to be scientific but really are not. The evidence of-fered is insufficient, and facts that point in other directions are given shortshrift. However, the ideas they offer survive and thrive because they speakThe Illusory Validity of Subjective Technical Analysis 41
to powerful emotional needs that science often leaves unfulfilled.”!” AsSagan said, it’s no fun to be skeptical. It is a burdensome attitude thatleaves our need to believe in fun and comforting ideas, like the tooth fairyand Santa Claus, unsatisfied.
COGNITIVE PSYCHOLOGY: HEURISTICS, BIASES,AND ILLUSIONS
 
Cognitive psychology is concerned with how we process information,draw conclusions, and make decisions. It studies the mental processes bywhich sensory input is transformed, reduced, elaborated, stored, and re-covered.!®
Over the last 30 years cognitive psychologies have been investigatingthe origins of unreliable knowledge. The good news is that common senseand intuitive interpretations of experience are mostly correct. The badnews is that human intelligence is maladapted to making accurate judg-ments in situations characterized by uncertainty. Under conditions of un-certainty, intuitive judgments and informally acquired knowledge areoften wrong. Because financial market behavior is highly uncertain, erro-neous knowledge in this domain is to be expected.
The pioneering research of Daniel Kahneman, Paul Slovic, and AmosTversky showed that illusory knowledge” originates in two ways. First,people are plagued by various cognitive biases and illusions that distortwhat we experience and how we learn from that experience. Second, tocompensate for the mind's limited abilities to process information, humanintelligence has evolved various mental shortcuts called judgment heuris-tics. These rules of thought, which operate quite automatically beneathour conscious awareness, are the basis of our intuitive judgments andprobability assessments. They are a mark of human intelligence crucial toeveryday living. Though these quick and dirty rules of thinking are gener-ally successful, in certain kinds of situation they cause us to make biaseddecisions and acquire erroneous knowledge.
Erroneous knowledge is especially problematic because of its re-silience. Studies have shown that once a belief has been adopted, it cansurvive the assault of new evidence contradicting it or even a completediscrediting of the original evidence that led to the belief’s formation.
The sections that follow will discuss a variety of cognitive errors re-sponsible for erroneous knowledge. For purposes of presentation, eachwill be discussed separately. In actuality, however, they operate con-jointly, feeding into and off one another, creating an illusion of validity forsubjective TA. This is illustrated in Figure 2.3.42 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS
 
 
 
 
 
 
 
 
    
 
 
 
 
   
 
  
 
  
 
 
 
 
 
 
 
 
 
 
oe Overconfidence, renSelf Attribution Optimism, Hindsightias Bias BiasKnowledge ConfirmationIllusion BiasILLUSION/ O |Illusion VALIDITYof +> IllusoryContro CorrelationBiased IllusorySecondhand Trends l¢—____| SaleKnowledgea Patterns Neglect
 
 
 
 
 
 
 
 
 
\ 2
RepresentativenessHeuristic Bias
 
 
 
 
FIGURE 2.3 Illusion of validity.
HUMAN INFORMATION PROCESSING LIMITATIONS
 
Despite its awesome power, the information-processing capabilities of thehuman brain are limited. Nobel Laureate Herbert Simon called this theprinciple of bounded rationality. “The capacity of the human mind for for-mulating and solving complex problems is very small compared with sizeof the problems whose solution is required.””’ Consequently, we attend toonly a small fraction of the torrent of information the world presents andprocess it in simplistic ways.
The number of separate chunks of information that can be held inconscious memory at a given time is estimated to be seven, plus or minustwo.”! Even more limited is the mind’s ability to handle problems requiringconfigural thinking. A configural-thinking problem requires that a multi-tude of factors (variables) be considered simultaneously as an inseparableconfiguration. Research indicates that