Proximate Mechanisms of Individual Decision-Making Behavior

In the early twentieth century, neoclassical economic theorists began to explore mathematical models of maximization. The theories of human behavior that they produced explored how optimal human agents, who were subject to no internal computational resource constraints of any kind, should make choices. During the second half of the twentieth century, empirical work laid bare the limitations of this approach. Human decision makers were often observed to fail to achieve maximization in domains ranging from health to happiness to wealth. Psychologists responded to these failures by largely abandoning holistic theory in favor of large-scale multi-parameter models that retained many of the key features of the earlier models. Over the last two decades, scholars combining neurobiology, psychology, economics, and evolutionary approaches have begun to examine alternative theoretical approaches. Their data suggest explanations for some of the failures of neoclassical approaches and revealed new theoretical avenues for exploration. While neurobiologists have largely validated the economic and psychological assumption that decision makers compute and represent a single-decision variable for every option considered during choice, their data also make clear that the human brain faces severe computational resource constraints which force it to rely on very specific modular approaches to the processes of valuation and choice.