Neuroeconomics: Using Neuroscience to Make Economic Predictions

Neuroeconomics seeks to ground economic theory in detailed neural mechanisms which are expressed mathematically and make behavioural predictions. One finding is that simple kinds of economising for life-and-death decisions (food, sex and danger) do occur in the brain as rational theories assume. Another set of findings appears to support the neural basis of constructs posited in behavioural economics, such as a preference for immediacy and nonlinear weighting of small and large probabilities. A third direction shows how understanding neural circuitry permits predictions and causal experiments which show state-dependence of revealed preference – except that states are biological and neural variables

Stated versus inferred beliefs: A methodological inquiry and experimental test

If asking subjects their beliefs during repeated game play changes the way those subjects play, using those stated beliefs to evaluate and compare theories of strategic behavior is problematic. We experimentally verify that belief elicitation can alter paths of play in a repeated asymmetric matching pennies game. In this setting, belief elicitation improves the goodness of fit of structural models of belief learning, and the prior beliefs implied by such structural models are both stronger and more realistic when beliefs are elicited than when they are not. These effects are, however, confined to the player type who sees a strong asymmetry between payoff possibilities for her two strategies in the game. We also find that “inferred beliefs” (beliefs estimated from past observed actions of opponents) can be better predictors of observed actions than the “stated beliefs” resulting from belief elicitation.beliefs; stated beliefs; belief elicitation; inferred beliefs; estimated beliefs; belief updating; repeated games; experimental methods

Computational phenotyping of two-person interactions reveals differential neural response to depth-of-thought.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans

Behavioral Economics

Behavioral economics uses evidence from psychology and other disciplines to create models of limits on rationality, willpower and self-interest, and explore their implications in economic aggregates. This paper reviews the basic themes of behavioral economics: Sensitivity of revealed preferences to descriptions of goods and procedures; generalizations of models of choice over risk, ambiguity, and time; fairness and reciprocity; non-Bayesian judgment; and stochastic equilibrium and learning. A central issue is what happens in equilibrium when agents are imperfect but heterogeneous; sometimes firms “repair” limits through sorting, but profit-maximizing firms can also exploit limits of consumers. Frontiers of research are careful formal theorizing about psychology and studies with field data. Neuroeconomics extends the psychological data use to inform theorizing to include details of neural circuitry. It is likely to support rational choice theory in some cases, to buttress behavioral economics in some cases, and to suggest different constructs as well

Mechanisms of Endogenous Institutional Change

This paper proposes an analytical-cum-conceptual framework for understanding the nature of institutions as well as their changes. In doing so, it attempts to achieve two things: First, it proposes a way to reconcile an equilibrium (endogenous) view of institutions with the notion of agents’ bounded rationality by introducing such concepts as a summary representation of equilibrium as common knowledge of agents. Second, it specifies some generic mechanisms of institutional coherence and change -- overlapping social embededdness, Schumpeterian innovation in bundling games and dynamic institutional complementarities -- useful for understanding the dynamic interactions of economic, political, social and organizational factors.

Trust and Learning

Learning to trust the right partner is pivotal to survival. But what information matters to decide whom to trust? In this chapter, we review evidence suggesting that different character traits play a role in the formation of trustworthiness impressions and beliefs that guide trust decisions. Learning of these traits depends on available information about the other person, previous knowledge, and contextual circumstances. Interestingly, when these factors favor the learning of particular traits, the resulting beliefs are harder to revise and lead to behavioral patterns that suggest a learning impairment. Computational models indicate an asymmetry in feedback valuation that is not due to the type of feedback (e.g., positive or negative) but rather to previous knowledge and contextual factors (e.g., the reputation of the other person). Neuroimaging studies highlight the role of mentalizing brain regions in building adequate mental models of others. Specifically, the orbitofrontal cortex and temporoparietal junction are central to the formation and updating of trustworthiness beliefs. Further, the dorsal posterior cingulate cortex and lateral frontoparietal regions likely underpin information integration processes for behavior change in face of untrustworthiness. We finally call for collaborative efforts in future scientific enterprises to develop a still lacking neurocomputational theory of social learning

Expanding Social Network Modeling Software and Agent Models for Diffusion Processes

In an increasingly digitally interconnected world, the study of social networks and their dynamics is burgeoning. Anthropologically, the ubiquity of online social networks has had striking implications for the condition of large portions of humanity. This technology has facilitated content creation of virtually all sorts, information sharing on an unprecedented scale, and connections and communities among people with similar interests and skills. The first part of my research is a social network evolution and visualization engine. Built on top of existing technologies, my software is designed to provide abstractions from the underlying libraries, drive real-time network evolution based on user-defined parameters, and optionally visualize that evolution at each step of the process. My software provides a low maintenance interface for the creation of networks and update schemes for a wide array of experimental contexts, an engine to drive network evolution, and a visualization platform to provide real-time feedback about different aspects of the network to the researcher, as well as fine-grained debugging tools. We conducted investigations into the opinion dynamics of networks when multiple agent “archetypes” interact together with this platform. We modeled agents’ archetypes with respect to two attributes: their preference over their friends’ opinion profiles, and their tendency to change their opinion over time. We extended the current state of agent modeling in opinion diffusion by providing a unified 2D trajectory/preference space for agents that incorporates most common models in the literature. We investigated six agent archetypes from this space, and examined the behavior of the network as a whole and the individual agents in a variety of contexts. In another branch of work using our software, we developed a network of agents who must carry out both economic and social activities during a pandemic. Agents’ decisions about what actions to take (self-protective measures like masking, social distancing, or waiting to run errands) are based on several factors, including perception of risk (obtained from news reports, social connections, etc.) and economic need. We show with preliminary testing that this platform is able to execute standard pandemic models successfully with the incorporation of the economic and social dimensions, and that this paradigm may provide useful insight into effective agent-level response policies that can be used in concert with other top-down approaches that comprise most of the recent pandemic response research. We have investigated the implications of varying behavior profiles within a network of agents, and how those behavioral compositions affect the overall climate of the network in return, and this software will continue to facilitate similar research into the future

Cheap Talk, Gullibility, and Welfare in an Environmental Taxation Game

We consider a simple dynamic model of environmental taxation that exhibits time inconsistency. There are two categories of firms, Believers, who take the tax announcements made by the Regulator to face value, and Non-Believers, who perfectly anticipate the Regulator's decisions, albeit at a cost. The proportion of Believers and Non- Believers changes over time depending on the relative profits of both groups. We show that the Regulator can use misleading tax announcements to steer the economy to an equilibrium that is Pareto superior to the solutions usually suggested in the literature. Depending upon the initial proportion of Believers, the Regulator may prefer a fast or a low speed of reaction of the firms to differences in Believers/Non-Believers profits.Environmental policy, Emissions taxes, Time inconsistency, Heterogeneous agents, Bounded rationality, Learning, Multiple equilibria, Stackelberg games