Networks pervade social and economic life, and they play a prominent role in explaining a huge variety of social and economic phenomena. Standard economic theory did not give much credit to the role of networks until the early 1990s, but since then the study of the theory of networks has blossomed. At the heart of this research is the idea that the pattern of connections between individual rational agents shapes their actions and determines their rewards. The importance of connections has (...) in turn motivated the study of the very processes by which networks are formed. In Connections, Sanjeev Goyal puts contemporary thinking about networks and economic activity into context. He develops a general framework within which this body of research can be located. In the first part of the book he demonstrates that location in a network has significant effects on individual rewards and that, given this, it is natural that individuals will seek to form connections to move the network in their favor. This idea motivates the second part of the book, which develops a general theory of network formation founded on individual incentives. Goyal assesses the robustness of current research findings and identifies the substantive open questions. Written in a style that combines simple examples with formal models and complete mathematical proofs, Connections is a concise and self-contained treatment of the economic theory of networks, one that should become the natural source of reference for graduate students in economics and related disciplines. (shrink)

In this article, we provide a tutorial overview of some aspects of statistical learning theory, which also goes by other names such as statistical pattern recognition, nonparametric classification and estimation, and supervised learning. We focus on the problem of two-class pattern classification for various reasons. This problem is rich enough to capture many of the interesting aspects that are present in the cases of more than two classes and in the problem of estimation, and many of the results can be (...) extended to these cases. Focusing on two-class pattern classification simplifies our discussion, and yet it is directly applicable to a wide range of practical settings. (shrink)

In _Reliable Reasoning_, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory, the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade (...) the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT. (shrink)

Stochastic forecasts in complex environments can benefit from combining the estimates of large groups of forecasters (“judges”). But aggregating multiple opinions faces several challenges. First, human judges are notoriously incoherent when their forecasts involve logically complex events. Second, individual judges may have specialized knowledge, so different judges may produce forecasts for different events. Third, the credibility of individual judges might vary, and one would like to pay greater attention to more trustworthy forecasts. These considerations limit the value of simple aggregation (...) methods like linear averaging. In this paper, a new algorithm is proposed for combining probabilistic assessments from a large pool of judges. Two measures of a judge’s likely credibility are introduced and used in the algorithm to determine the judge’s weight in aggregation. The algorithm was tested on a data set of nearly half a million probability estimates of events related to the 2008 U.S. presidential election (∼ 16000 judges). (shrink)

In order to consider how best to address relationship concerns with potential research participants arising in this study, we will first describe unique features...

The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not.

As part of an article symposium on their “Narrow Identities”, Partha Dasgupta and Sanjeev Goyal respond to commentaries by Jean-Paul Carvalho, John B. Davis, Peter Finke, and Miriam Teschl.

��The Coherent Approximation Principle (CAP) is a method for aggregating forecasts of probability from a group of judges by enforcing coherence with minimal adjustment. This paper explores two methods to further improve the forecasting accuracy within the CAP framework and proposes practical algorithms that implement them. These methods allow flexibility to add fixed constraints to the coherentization process and compensate for the psychological bias present in probability estimates from human judges. The algorithms were tested on a data set of nearly (...) half a million probability estimates of events related to the 2008 U.S. presidential election (from about 16000 judges). The results show that both methods improve the stochastic accuracy of the aggregated forecasts compared to using simple CAP. (shrink)

We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifi es the connection of coherence and proper scoring rules to Bregman divergence.

Statistical Learning Theory (e.g., Hastie et al., 2001; Vapnik, 1998, 2000, 2006) is the basic theory behind contemporary machine learning and data-mining. We suggest that the theory provides an excellent framework for philosophical thinking about inductive inference.

Like Glenn Shafer, we are nostalgic for the time when “philosophers, mathematicians, and scientists interested in probability, induction, and scientific methodology talked with each other more than they do now”, [p.10]. 1 Shafer goes on to mention other relevant contemporary communities. He himself has been at the interface of many of these communities while at the same time making major contributions to them and this very symposium represents something of that desired discussion. We begin with a couple of general points (...) about issues several commentators have raised and then discuss other more particular issues. (shrink)

Rett syndrome (RTT) is an X‐linked dominant neurodevelopmental disorder affecting almost exclusively girls. Although mutations in methyl‐CpG‐binding protein (MeCP2) are known to be associated with RTT, gene expression patterns are not significantly altered in MeCP2‐deficient cells. A recent study1 identified MeCP2‐mediated histone modification and formation of a higher‐order chromatin loop structure specifically associated with silent chromatin at the Dlx5–Dlx6 locus in normal cells, and its absence thereof in RTT patients. This altered expression of Dlx5 through loss of silent chromatin loop (...) formation provides a molecular mechanism underlying RTT and proposes a novel role for MeCP2 in chromatin organization and imprinting. © 2005 Wiley Periodicals, Inc. BioEssays 27:676–680, 2005. © 2005 Wiley Periodicals, Inc. (shrink)

Cognitive Science, Volume 46, Issue 1, January 2022.

This paper analyzes individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, ours is the first study of electoral forecasting to involve multiple subnational predictions and to incorporate the influence of respondents’ home states. We relate a range of demographic, political, and cognitive variables to individual accuracy and predictions, as well as to how accuracy improved over time. We find strong support for wishful thinking bias in expectations, as (...) Republicans gave higher probabilities to McCain victories and were worse at overall prediction. In addition, we find that respondents living in states with higher vote shares for Obama performed better at prediction and displayed less wishful thinking bias. We conclude by showing that suitable aggregations of our respondents’ predictions outperformed Intrade (a prediction market) and fivethirtyeight.com (a poll-based forecast) at most points in time. (shrink)

INDUS-EM is India’s only level one conference imparting and exchanging quality knowledge in acute care. Specifically, in general and specialized emergency care and training in trauma, burns, cardiac, stroke, environmental and disaster medicine. It provides a series of exchanges regarding academic development and implementation of training tools related to developing future academic faculty and residents in Emergency Medicine in India. The INDUS-EM leadership and board of directors invited scholars from multiple institutions to participate in this advanced educational symposium that was (...) held in Thrissur, Kerala in October 2013. (shrink)

As part of an article symposium on Partha Dasgupta and Sanjeev Goyal’s “Narrow Identities”, Jean-Paul Carvalho reflects on the concept of narrow identity and the ways of modelling its emergence.

As part of an article symposium on Partha Dasgupta and Sanjeev Goyal’s “Narrow Identities”, Peter Finke offers a critical anthropological perspective on the concept of social identity and its modeling in economics.

As part of an article symposium on Partha Dasgupta and Sanjeev Goyal’s “Narrow Identities”, John B. Davis reflects on the variety of social identities and the implications this variety has for social identity analysis.

As part of an article symposium on Partha Dasgupta and Sanjeev Goyal’s “Narrow Identities”, Miriam Teschl reflects on the distinctive concept of identity liberal cosmopolitans have and how it may or may not be captured in economic models of identity choice.