Speaker: Dr. David Banks, Duke University
Title: Adversarial Risk Analysis
Abstract: Adversarial Risk Analysis (ARA) is a Bayesian alternative to classical game theory. Rooted in decision theory, one builds a model for the decision-making of one's opponent, placing subjective distributions over all unknown quantities. Then one chooses the action that maximizes expected utility. This approach aligns with some perspectives in modern behavioral economics, and enables principled analysis of novel problems, such as a multiparty auction in which there is no common knowledge and different bidders have different opinion about each other. .... "
Here are the slides from the talk (Technical)
Good talk. In particular because it dealt with how people work in interactions. Either versus nature, or versus other humans, here in some sort of competition. The most common game example used was the Auction. In the cases described these were adversarial. Under the structure of this 'game' to win the auction. Humans in any interaction build a model of who they are interacting with. The methods proposed construct numerical methods to define the value of alternative strategies.
But it immediately came to mind that these methods don't need to be adversarial. When people converse, or ask for help from an adviser (Human or machine). they are looking to maximize the value of the interaction. In a chatbot, for example a person wants to solve a problem, the chatbot has defined knowledge. How should the interaction proceed? How should these advisory approaches be strategically designed? Under constraints and costs? How do we rate Assistants in an approach to provide value? Whats the structure of that game? Examining.
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