BAYESIAN HERDERS: ASYMMETRIC UPDATING OF RAINFALL BELIEFS IN RESPONSE TO EXTERNAL FORECASTS

Temporal climate risk weighs heavily on many of the world's poor. Recent advances in model-based climate forecasting have expanded the range, timeliness and accuracy of forecasts available to decision-makers whose welfare depends on stochastic climate outcomes. There has consequently been considerable recent investment in improved climate forecasting for the developing world. Yet, in cultures that have long used indigenous climate forecasting methods, forecasts generated and disseminated by outsiders using unfamiliar methods may not readily gain the acceptance necessary to induce behavioral change. The value of model-based climate forecasts depends critically on the premise that forecast recipients actually use external forecast information to update their rainfall expectations. We test this premise using unique survey data from pastoralists and agropastoralists in southern Ethiopia and northern Kenya, specifying and estimating a model of herders updating seasonal rainfall beliefs. We find that those who receive and believe model-based seasonal climate forecasts indeed update their priors in the direction of the forecast received, assimilating optimistic forecasts more readily than pessimistic forecasts.Agribusiness, O1, D1, Q12,

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Character and theory of mind: an integrative approach

Traditionally, theories of mindreading have focused on the representation of beliefs and desires. However, decades of social psychology and social neuroscience have shown that, in addition to reasoning about beliefs and desires, human beings also use representations of character traits to predict and interpret behavior. While a few recent accounts have attempted to accommodate these findings, they have not succeeded in explaining the relation between trait attribution and belief-desire reasoning. On my account, character-trait attribution is part of a hierarchical system for action prediction, and serves to inform hypotheses about agents’ beliefs and desires, which are in turn used to predict and interpret behavior

Who Becomes a Stockholder? Expectations, SUbjective Uncertainty, and Asset Allocation

We develop a model of portfolio selection with subjective uncertainty and learning in order to explain why some people hold stocks while others don’t. We model heterogeneity in information directly, which is an alternative to the existing explanations that emphasized heterogeneity in transaction costs of investment. We plan to calibrate the model to survey data (when available) on people’s perception about the distribution of stock market returns. Our approach also leads to a model of learning with new implications such as zero optimal risky assets, or ex post correlation of uncorrelated labor income and optimal portfolio composition. It also points to two factors in probabilistic thinking that should have a major impact on stock ownership. These are the level and the precision of expectations. We construct proxy measures for the two parameters from the 1992-2000 waves of the Health and Retirement Study (HRS). We use a large battery of the subjective probability questions administered in each wave of HRS to construct an overall “index of optimism” (the correlated factor between all subjective probabilities) and “index of precision” (the fraction of nonfocal probability answers, following Lillard and Willis, 2001). We also construct measures for how people forecast the weather, their cognitive capacity, wealth, and basic demographics. Our results indicate that stock ownership and the probability of becoming a stockholder are strongly positively correlated with the indices of the level and precision of expectations. Interpretation of the former is quite challenging and further research is needed to understand its full content.

How Homophily Affects Learning and Diffusion in Networks

We examine how three different communication processes operating through social networks are affected by homophily - the tendency of individuals to associate with others similar to themselves. Homophily has no effect if messages are broadcast or sent via shortest paths; only connection density matters. In contrast, homophily substantially slows learning based on repeated averaging of neighbors' information and Markovian diffusion processes such as the Google random surfer model. Indeed, the latter processes are strongly affected by homophily but completely independent of connection density, provided this density exceeds a low threshold. We obtain these results by establishing new results on the spectra of large random graphs and relating the spectra to homophily. We conclude by checking the theoretical predictions using observed high school friendship networks from the Adolescent Health dataset.Networks, Learning, Diffusion, Homophily, Friendships, Social Networks, Random Graphs, Mixing Time, Convergence, Speed of Learning, Speed of Convergence

Risk, uncertainty and pasture investment decisions

The private decisions of farmers to invest in new technologies interest economists because these decisions influence the rate of farm productivity growth and the returns to public investment in agricultural research and development. Economic analysis of decisions to invest in new technologies on farms involves considering the effects of these decisions on the profitability and risk of the farm business. This is done routinely using whole-farm economic models and techniques such as stochastic simulation. Such analysis can be used to predict the extent to which a technology is likely to be adopted in equilibrium, when the consequences of adoption are known to all potential adopters. Until this equilibrium is reached, however, potential adopters of new technologies face uncertainty about the consequences of adoption. This alters expectations about the effects on profitability and risk of adoption, and hence alters investment decisions. The resolution of uncertainty over time through learning is therefore a key determinant of the rate at which new technologies are adopted, and hence should be represented in dynamic economic models which seek to explain these decisions.Farm Management,

The Influence of Affect on Beliefs, Preferences and Financial Decisions

Recent research in neuroeconomics suggests that the same brain areas that generate emotional states are also involved in the processing of information about risk, rewards and punishments. These findings imply that emotions may influence financial decisions in a predictable and parsimonious way. Our evidence suggests that affect -- generated either by exogenous manipulations, or endogenously by outcomes of prior actions -- indeed matters for financial risk taking, and that it does so by changing preferences as well as the belief formation process. Positive and arousing emotional states such as excitement induce people to take more risk, and to be more confident in their ability to evaluate the available investment options, relative to neutral states, while negative emotions such as anxiety have the opposite effects. Moreover, beliefs are updated in a way that is consistent with the self-preservation motive of maintaining positive affect and avoiding negative affect, by not fully taking into account new information that is at odds with the individuals' prior choices. Therefore, characteristics of markets, economic policies or organization design that have an impact on emotional brain circuits may influence decision making and affect important outcomes at the individual and aggregate level.affect, emotions, beliefs, risk taking, learning, limbic system, neuroeconomics, neurofinance