Evolution of Entrepreneurial Judgment with Venture-Specific Experience

This study advances research on entrepreneurial cognition by investigating how entrepreneurial judgment evolves during new venture creation. We conceptualize entrepreneurial judgment as a cognitive process in the minds of entrepreneurs that operates on the causal map – i.e., a knowledge structure concerning what factors they believe will help the chances of profitability under uncertainty. At the time of initial epiphany, entrepreneurs construct a cognitive causal map which guides resource allocation decisions. Over time, venture-specific experience accumulates and entrepreneurial judgment evolves in response to their observations. Using a dataset of 524 nascent entrepreneurs, we find that entrepreneurs with more venturespecific experiences have more selective judgments, and have stronger conviction in those judgments. We also find that perceived uncertainty and cognitive dispositions of the individuals affect entrepreneurial judgment

Getting to know you: Accuracy and error in judgments of character

Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate

What science can teach us about “Enhanced Interrogation”

No abstract available

Perceiving Pain in Others: Automatic and Controlled Mechanisms

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Bayesian Inference of Social Norms as Shared Constraints on Behavior

People act upon their desires, but often, also act in adherence to implicit social norms. How do people infer these unstated social norms from others' behavior, especially in novel social contexts? We propose that laypeople have intuitive theories of social norms as behavioral constraints shared across different agents in the same social context. We formalize inference of norms using a Bayesian Theory of Mind approach, and show that this computational approach provides excellent predictions of how people infer norms in two scenarios. Our results suggest that people separate the influence of norms and individual desires on others' actions, and have implications for modelling generalizations of hidden causes of behavior.Comment: 7 pages, 5 figures, to appear in CogSci 2019, code available at https://github.com/ztangent/norms-cogsci1

Explanatory Challenges in Metaethics

There are several important arguments in metaethics that rely on explanatory considerations. Gilbert Harman has presented a challenge to the existence of moral facts that depends on the claim that the best explanation of our moral beliefs does not involve moral facts. The Reliability Challenge against moral realism depends on the claim that moral realism is incompatible with there being a satisfying explanation of our reliability about moral truths. The purpose of this chapter is to examine these and related arguments. In particular, this chapter will discuss four kinds of arguments – Harman’s Challenge, evolutionary debunking arguments, irrelevant influence arguments, and the Reliability Challenge – understood as arguments against moral realism. The main goals of this chapter are (i) to articulate the strongest version of these arguments; (ii) to present and assess the central epistemological principles underlying these arguments; and (iii) to determine what a realist would have to do to adequately respond to these arguments

Searching for rewards in graph-structured spaces

How do people generalize and explore structured spaces? We study human behavior on a multi-armed bandit task, where rewards are influenced by the connectivity structure of a graph. A detailed predictive model comparison shows that a Gaussian Process regression model using a diffusion kernel is able to best describe participant choices, and also predict judgments about expected reward and confidence. This model unifies psychological models of function learning with the Successor Representation used in reinforcement learning, thereby building a bridge between different models of generalization