Search and Active Learning with Correlated Information: Empirical Evidence from Mid-Atlantic Clam Fishermen

This paper examines search with active learning and correlated information. We first develop a simple model to show how correlation affects the decision to acquire information. A unique data set on fishing site choice by mid-Atlantic clam fishermen is used to test the model predictions. Results find that clam fishermen search new sites when the catch at familiar sites declines, i.e., when the opportunity cost of gathering information is low, but also when catch at familiar sites is on the rise. Search following a catch decline occurs at spatially distant sites whereas search following a catch increase occurs at nearby sites. Correlated learning is crucial for explaining the site choice patterns in our data. These results provide new insights that may extend to a variety of economic search problems where correlated learning is important.

Formal models of source reliability

The paper introduces, compares and contrasts formal models of source reliability proposed in the epistemology literature, in particular the prominent models of Bovens and Hartmann (2003) and Olsson (2011). All are Bayesian models seeking to provide normative guidance, yet they differ subtly in assumptions and resulting behavior. Models are evaluated both on conceptual grounds and through simulations, and the relationship between models is clarified. The simulations both show surprising similarities and highlight relevant differences between these models. Most importantly, however, our evaluations reveal that important normative concerns arguably remain unresolved. The philosophical implications of this for testimony are discussed

Potentials and Limits of Bayesian Networks to Deal with Uncertainty in the Assessment of Climate Change Adaptation Policies

Bayesian networks (BNs) have been increasingly applied to support management and decision-making processes under conditions of environmental variability and uncertainty, providing logical and holistic reasoning in complex systems since they succinctly and effectively translate causal assertions between variables into patterns of probabilistic dependence. Through a theoretical assessment of the features and the statistical rationale of BNs, and a review of specific applications to ecological modelling, natural resource management, and climate change policy issues, the present paper analyses the effectiveness of the BN model as a synthesis framework, which would allow the user to manage the uncertainty characterising the definition and implementation of climate change adaptation policies. The review will let emerge the potentials of the model to characterise, incorporate and communicate the uncertainty, with the aim to provide an efficient support to an informed and transparent decision making process. The possible drawbacks arising from the implementation of BNs are also analysed, providing potential solutions to overcome them.Adaptation to Climate Change, Bayesian Network, Uncertainty