The Role of Social Cognition and Person Perception in Economic Social Decision-Making

Social decision-making is often complex, requiring the decision-maker to make social inferences about another person in addition to engaging traditional decision-making processes. However, until recently, much research in neuroeconomics and behavioral economics has examined social decision-making while failing to take into account the importance of the social context and social cognitive processes that are engaged when viewing another person. Using social psychological theory to guide our hypotheses, four research studies investigate the role of social cognition and person perception in guiding economic decisions made in social contexts. The first study (Chapter 2) demonstrates that only specific types of social information engage brain regions implicated in social cognition and hinder learning in social contexts. Study 2 (Chapter 3) extends these findings and examines contexts in which this social information is used to generalize across contexts to form predictions about another person’s behavior. Study 3 (Chapter 4) demonstrates that under certain contexts these social cognitive processes may be withheld in order to more effectively complete the task at hand. Last, Study 4 (Chapter 5) examines how this knowledge of social cognitive processing can be used to change behavior in a prosocial group context. Taken together, these studies add to the growing body of literature examining decision-making in social contexts and highlight the importance of social cognitive processing in guiding these decisions. Although social cognitive processing typically facilitates social interactions, these processes may alter economic decision-making in social contexts.

Sequential anomaly detection in the presence of noise and limited feedback

This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.

Considering the role of social dynamics and positional behavior in gestural communication research.

While the hominin fossil record cannot inform us on either the presence or extent of social and cognitive abilities that may have paved the way for the emergence of language, studying non-vocal communication among our closest living relatives, the African apes, may provide valuable information about how language originated. Although much has been learned from gestural signaling in non-human primates, we have not yet established how and why gestural repertoires vary across species, what factors influence this variation, and how knowledge of these differences can contribute to an understanding of gestural signaling's contribution to language evolution. In this paper, we review arguments surrounding the theory that language evolved from gestural signaling and suggest some important factors to consider when conducting comparative studies of gestural communication among African apes. Specifically, we propose that social dynamics and positional behavior are critical components that shape the frequency and nature of gestural signaling across species and we argue that an understanding of these factors could shed light on how gestural communication may have been the basis of human language. We outline predictions for the influence of these factors on the frequencies and types of gestures used across the African apes and highlight the importance of including these factors in future gestural communication research with primates.