Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds.  present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution.  apply this approach to a dynamic decision-making problem where artificial agents make decisions about the source of incoming information. In doing so,  show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies.
['Peter D. Kvam', 'Joseph Cesario', 'Jory Schossau', 'Heather L. Eisthen', 'Arend Hintze']
I am writing a research paper on a idea Computational evolution of decision-making strategies
[{"from": "human", "value": "I am writing a research paper on a idea Computational evolution of decision-making strategies"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Most research on adaptive decision-making takes a strategy-first approach, proposing a method of solving a problem and then examining whether it can be implemented in the brain and in what environments it succeeds.  present a method for studying strategy development based on computational evolution that takes the opposite approach, allowing strategies to develop in response to the decision-making environment via Darwinian evolution.  apply this approach to a dynamic decision-making problem where artificial agents make decisions about the source of incoming information. In doing so,  show that the complexity of the brains and strategies of evolved agents are a function of the environment in which they develop. More difficult environments lead to larger brains and more information use, resulting in strategies resembling a sequential sampling approach. Less difficult environments drive evolution toward smaller brains and less information use, resulting in simpler heuristic-like strategies."}]