Neuroeconomics is the result from the cross-breeding of several allied fields, Psychology, Nuerobiology and Normative Economics.
Human agents have the unique capacity to abstract from learning. This is due to the fact that agents are able to identify patterns consisting of individual contexts to find elements of unity among different situations and items or events similar to one experienced in past. As such, there is a definite relationship between learning and abstraction where learning requires the capacity to make abstraction in terms of context recognition that help generalization of learned information about the complex world. However, the capacity to analyze or compute infinite amount of information and variables are constrained due to limitation in human capacity to abstract and generalize since, the capacity to store information as memory is arbitrarily fixed for all individuals although, there may be differences in human short-term and long-term memory capacity utilization that results in reactionary variations in human behavior. This limitation in computation process of the human brain limits human rationality where, human behavior is modeled in a more realistic world. This invariably results in rationality of bounds in information use that limits cognitive capacities in agent learning process. However, abstraction is a critical step toward efficient processing of information and to understand the interactions between action, perception and reaction.
Neuroeconomics comes into play in this scenario to bridge the gap between neuroscience and microeconomic models of human decision-making. Yet, one might ask, why neuroeconomics? Where is the need and why is the need (Camerer , 2004)? This is a relatively new domain that helps us to understand how our brain, as rational agent, make choices, take decisions and learn from experiences. In order to maximize the outcome from decision-making, we try to choose the best possible alternative(s) from some given options but this process of choosing is not simple. We are constrained by the capacity to absorb all the information presented to us all at the same time, and hence, our brain process that information given to us to come to some conclusion based on which we make decisions. Herein, neuroeconomics can inform economics (Camerer, Loewenstein, Prelec, 2005) how our brain helps us to do things that we want to do and make choices, and to understand the neurobiological basis of informed decision-making. It also helps us to understand how rational agents learn and under what conditions such learning would maximize efficiency in information processing (Chatterjee, 2011).
To unite human behavioral sciences with the microeconomic models of agent behavior, Neuroeconomics hence, can be defined as according to Kevin McCabe and Daniel Houser (2008) "Neuroeconomics is an interdisciplinary research program with the goal of building a biological model of decision making in economic environments." Simply put, "Neuroeconomics is the study of how the brain makes economic decisions (Kevin McCabe and Daniel Houser, 2008)."
Microeconomic models generally include rational behavior of agents as rule following under bounded rationality (Simon, 1976). It determines the need to simply represent the behavior of a system with a few number of variables and limited information for ease of generalization given the notion that human incapacity to see differences among infinite inferences in a complex world. The mechanism of rule following under bounded rationality and selection of rules results in competition among rules which help select more advantageous ones, since rule following is the best way to survive due to limitations in human rationality. Frugal heuristics which are rules that use fewer information under bounded rationalities, also limits the perception of differences among situations thus enhancing the capacity to generalize to deal with a complex world which reduces the amount of variables to manage. These cognitive limitations in turn, stimulate adaptive decisions by optimizing techniques for using unlimited amount of information. This calls for the need to learn that further aids in enlarging the maps of bounded rationality, where, agents will have the options of choice among as many variables. This also envisages the fact that why everyone is not alike since, agents either use different rules leading to different trajectories or outcomes, or they use different variables based on dissimilar sets of information that gives rise to variations in agent behavior, or they do not learn to use the rules at all! Research on rule based behavior and rationality as well as on bounded rational agents indicates that bounded rationality is an adaptive capacity which enables learning. This is different from the teleological point of view where learning is not governed by bounded rationality but founded on unbounded learning process. Bounded rationality implies rule following.
Why is the Need?
Globalization has placed an inherent tendency toward increasing the knowledge domains. More interdisciplinary dimensions are being created due to rapid evolution of knowledge generation processes and innovative learning technologies. Today, there are as many more variables available than ever before at our disposal as both quantitative facts and soft qualitative facts. Unlimited growth of knowledge and information has however imposed limitations in understanding fully agent behavior due to complexity of technical optimality. However, it shall be born in mind that learning and knowledge dissipation brings on social welfare, both for the state and its agents. There have been increased role of large institutions in knowledge generation shaping the long-term trends in learning and social affair. But with tremendous increase in knowledge generation, the nature and quantity of information presented to our brain is enormous, and to keep track of all those, it is not possible for the brain to absorb all those information and process all those all at once. Human memory has limitations, and the processing capacity of our brain is also limited. So, in order to get the maximum benefit out of such processing, our brain filters and organizes the information presented (by perception, procession and learning) and processes what that is required most, which means, there is a rational boundary beyond which our brain cannot accommodate or process all that is presented to it. So it has limitations, or constraints. This is why the concept of bounded rationality was put forward by Herbert Simon in 1976. By the process of learning, brain organizes such information which however helps to increase the efficiency of information processing. Neuroeconomics underpins the neurobiological basis of such information processing in the brain with the aid of advanced imaging techniques like fMRI to pinpoint and look from the outside what is happening in the inside of the brain. Hence, there is some definite need for understanding how neural processes helps us to make decisions to understand the neural basis of information processing and so is the need for neuroeconomics to bridge the gap.