Ran Spiegler

We present an equilibrium model of politics in which political platforms compete over public opinion. A platform consists of a policy, a coalition of social groups with diverse intrinsic attitudes to policies, and a narrative. We conceptualize narratives as subjective models that attribute a commonly valued outcome to (potentially spurious) postulated causes. When quantified against empirical observations, these models generate a shared belief among coalition members over the outcome as a function of its postulated causes. The intensity of this belief and the members’ intrinsic attitudes to the platform’s policy determine the extent to which the coalition is mobilized. Only platforms that generate maximal mobilization prevail in equilibrium. Our equilibrium characterization demonstrates how false narratives can be detrimental to the commonly valued outcome, and how political fragmentation leads to their proliferation. The false narratives that emerge in equilibrium have a flavor of “scapegoating:” They attribute good outcomes to the exclusion of social groups from ruling coalitions.

Behavioral economics is widely perceived to be part of the profession’s shift away from a culture that places abstract theory at its center. I present a critical discussion of the atheoretical style with which “behavioral” themes are often disseminated: a purely anecdotal cost-benefit modeling in pieces that target a wide audience of academic economists, and the practice of capturing psychological forces by distorting familiar functional forms. I argue that the subject of “ psychology and economics” is intrinsically foundational, and that a heavier dose of abstract theorizing is essential for it to realize its transformative potential. (JEL B40, D90)

An agent forms estimates (or forecasts) of individual variables conditional on some observed signal. His estimates are based on fitting a subjective causal model—formalized as a directed acyclic graph, following the “Bayesian networks” literature—to objective long-run data. I show that the agent’s average estimates coincide with the variables’ true expected value (for any underlying objective distribution) if and only if the agent’s graph is perfect—that is, it directly links every pair of variables that it perceives as causes of some third variable. This result identifies neglect of direct correlation between perceived causes as the kind of causal misperception that can generate systematic prediction errors. I demonstrate the relevance of this result for economic applications: speculative trade, manipulation of a firm’s reputation, and a stylized “monetary policy” example in which the inflation-output relation obeys an expectational Phillips Curve.

We formalize the argument that political disagreements can be traced to a "clash of narratives". Drawing on the "Bayesian Networks" literature, we model a narrative as a causal model that maps actions into consequences, weaving a selection of other random variables into the story. An equilibrium is defined as a probability distribution over narrative-policy pairs that maximizes a representative agent's anticipatory utility, capturing the idea that public opinion favors hopeful narratives. Our equilibrium analysis sheds light on the structure of prevailing narratives, the variables they involve, the policies they sustain and their contribution to political polarization.

I present a behavioral model of a .data analyst.who extrapolates a fully speci.ed probability distribution over observable variables from a collection of statistical datasets that cover partially overlapping sets of variables. The analyst employs an iterative extrapolation procedure, whose individual rounds are akin to the stochastic-regression method of imputing missing data. Users of the procedure's output fail to distinguish between raw and imputed data, and it functions as their practical belief. I characterize the ways in which this belief distorts the correlation structure of the underlying data generating process - focusing on cases in which the distortion can be described as the imposition of a causal model (represented by a directed acyclic graph over observable variables) on the true distribution.

Consumers often find it hard to make correct value comparisons between market alternatives. Part of this choice complexity is the result of deliberate obfuscation by firms. This review synthesizes a theoretical literature that analyzes the role of choice complexity in otherwise competitive markets. I identify two general classes of market models in the literature: (a) The obfuscation strategy of firms is an independent framing device that affects the probability with which consumers make correct comparisons, and (b) market alternatives are multiattribute objects, and obfuscation is captured by lopsided location in attribute space, lowering the probability of being dominated by another market alternative. I address the following key questions: What determines the amount of choice complexity in market equilibrium? What is the relation between choice complexity and payoff-relevant aspects of the market outcome? What is the role of consumer protection measures? The models surveyed in this review suggest that equilibrium obfuscation and choice complexity increase in response to intensified competition, mitigating the positive effect of competition on consumer welfare. However, equilibrium effects can also attenuate the positive welfare effects of regulatory interventions.

I present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). He is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels. The DM attempts to fit this model to p, resulting in a subjective belief that distorts p by factorizing it according to the graph via the standard Bayesian-network formula. As a result of this belief distortion, the DM's evaluation of actions can vary with their long-run frequencies. Accordingly, I define a "personal equilibrium" notion of individual behavior. The framework enables simple graphical representations of causal-attribution errors (such as coarseness or reverse causation), and provides tools for checking rationality properties of the DM's behavior. I demonstrate the framework's scope of applications with examples covering diverse areas, from demand for education to public policy.