DAS presents - Arbitrage and Rational Decisions

This talk will present a brief overview of some key topics and results in Bob’s research that are discussed in his recently published book, of which a draft copy is available for free on his web site. These topics include the importance of having money (in your mind as well as in your pocket), the centrality of the separating-hyperplane theorem in all of the main branches of rational choice theory (it’s where the numbers come from), the intrinsic imprecision and inseparability of subjective probabilities and utilities (with apologies to Ramsey and Savage), the use of risk neutral probabilities (a tool from finance) in modeling aversion to ambiguity as well as risk, and the direct unification of game theory with decision analysis and finance theory via the common rationality principle of no-arbitrage. Among the findings are tools for decision modeling that do not require separating probabilities from utilities, a generalization of the Arrow-Pratt risk aversion measure that applies to non-expected-utility preferences, a so-called smooth model of ambiguity-averse preferences (as exhibited in Ellsberg’s paradox), and a rationale for considering correlated equilibrium rather than Nash equilibrium as the fundamental solution concept of noncooperative game theory.

Maximizing Your INFORMS Membership - Industry

Orientation and Q&A for new members focusing on Industry members.

Decisions Under Uncertainty: How to say 'I'm not sure' without losing your job

Webinar with Evan Wimpey, Director of Analytics, PyMC Most important decisions are made under uncertainty. Yet most organizations behave as if uncertainty is something to be hidden, ignored, or smoothed away. In this session, Evan will examine why uncertainty so often gets pushed out of analytical workflows and decision conversations, even in the most "data driven" companies. We'll take a brief look at how uncertainty has been treated historically in analytics, and some practical tools and decision frameworks that help teams reason through risk. The goal isn't to eliminate uncertainty and demand perfect models. It's to make uncertainty visible, usable, and clear. Come listen, and I'm certain you'll enjoy it!