Expected subjective value theory (ESVT): A representation of decision under risk and certainty.

We present a descriptive model of choice derived from neuroscientific models of efficient value representation in the brain. Our basic model, a special case of Expected Utility Theory, can capture a number of behaviors predicted by Prospect Theory. It achieves this with only two parameters: a time-indexed “payoff expectation”(reference point) and a free parameter we call “predisposition”. A simple extension of the model outside the domain of Expected Utility also captures the Allais Paradox. Our models shed new light on the computational origins and evolution of risk attitudes and aversion to outcomes below reward expectation (reference point). It delivers novel explanations of the endowment effect, the observed heterogeneity in probability weighting functions, and the Allais Paradox, all with fewer parameters and higher descriptive accuracy than Prospect Theory

Adaptive Economics: A neuroethological approach to the study of preferences, biases, and choice

A neuron's curse is that at every given time, with the information available to it, it must choose to either send a signal to its neighbouring cells or remain silent. It has evolved to be the optimal decision unit and, together with around 86 billion of its neighbours, the neuron keeps us alive, helps us cooperate, and allows us to successfully compete with others when resources get scarce. Yet, we, being collections of these neurons, still struggle to describe how these individual decision-makers support the broader process that is human decision-making. Traditionally, decision theory has sought to understand human choices by relying more on mathematics than biology. This has led to the general assumption that decision-makers behave ‘as-if’ guided by mathematical rules and algorithms that are mostly static over time. In reality, however, decision-making relies on a brain that, due to its limited capacity, has evolved the ability for flexible and dynamic cognition. The experiments presented in this thesis, build on dichotomies in human behaviour that cannot be explained by traditional economic models - first replicating these findings in rhesus macaques, then addressing the neurobiological algorithms that could reconcile these dichotomies. Specifically, I looked at the effects of different reward ranges, different levels of risk, and different experimental paradigms in shaping the way monkeys made choices. I demonstrate that, far from having the stable and fixed preferences prescribed by economic models, rhesus macaques appear to flexibly adapt their choice preferences in a way that optimizes their decision-making given their experience with the task at hand. I then elaborate on the neurobiological basis for preference adaptation, and show how incorporating simple, dynamic algorithms into economic choice models improves their predictive power. Taken together, my results demonstrate the need for, and advantage of, integrating neuroethological thought into the current framework of decision theory.This work was made possible by funding from the European Research Council and the Wellcome Trust

Persuasion, Political Warfare, and Deterrence: Behavioral and Behaviorally Robust Models

This dissertation examines game theory models in the context of persuasion and competition wherein decision-makers are not completely rational by considering two complementary threads of research. The first thread of research pertains to offensive and preemptively defensive behavioral models. Research in this thread makes three notable contributions. First, an offensive modeling framework is created to identify how an entity optimally influences a populace to take a desired course of action. Second, a defensive modeling framework is defined wherein a regulating entity takes action to bound the behavior of multiple adversaries simultaneously attempting to persuade a group of decision-makers. Third, an offensive influence modeling framework under conditions of ambiguity is developed in accordance with historical information limitations, and we demonstrate how it can be used to select a robust course of action on a specific, data-driven use case. The second thread of research pertains to behavioral and behaviorally robust approaches to deterrence. Research in this thread makes two notable contributions. First, we demonstrate the alternative insights behavioral game theory generates for the analysis of classic deterrence games, and explicate the rich analysis generated from its combined use with standard equilibrium models. Second, we define behaviorally robust models for an agent to use in a normal form game under varying forms of uncertainty in order to inform deterrence policy decisions

Waterfall illusion in risky choice – exposure to outcome-irrelevant gambles affects subsequent valuation of risky gambles

Based on recent discoveries in economics, neuroscience, and psychology, we hypothesize that pure exposure to high-payoff or low-payoff gambles can change people's subsequent reported valuations of gambles and confirm this hypothesis in a laboratory experiment. In particular, the same participants within the same experimental session provide higher valuations for the same gambles after they have been exposed to low-payoff gambles compared to after they have been exposed to high-payoff gambles. These results are consistent with the current understanding of how the nervous system encodes payoffs and imply that even brief experiences that do not change wealth can impact an individual's reported valuations of risky options