von Neumann-Morgenstern and Savage Theorems for Causal Decision Making

Causal thinking and decision making under uncertainty are fundamental aspects of intelligent reasoning. Decision making under uncertainty has been well studied when information is considered at the associative (probabilistic) level. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rational choice using purely associative information. Causal inference often yields uncertainty about the exact causal structure, so we consider what kinds of decisions are possible in those conditions. In this work, we consider decision problems in which available actions and consequences are causally connected. After recalling a previous causal decision making result, which relies on a known causal model, we consider the case in which the causal mechanism that controls some environment is unknown to a rational decision maker. In this setting we state and prove a causal version of Savage's Theorem, which we then use to develop a notion of causal games with its respective causal Nash equilibrium. These results highlight the importance of causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc

Maximizing Influence in a Competitive Social Network: A Follower's Perspective

Maximizing Influence in a Competitive Social Network: A Follower's Perspectiv

Playing strategically against nature? – Decisions viewed from a game-theoretic frame

Common research on decision-making investigates non-interdependent situations, i.e., “games against nature”. However, humans are social beings and many decisions are made in social settings, where they mutually influence each other, i.e., “strategic games”. Mathematical game theory gives a benchmark for rational decisions in such situations. The strategic character makes psychological decision-making more complex by introducing the outcomes for others as an additional attribute of that situation; it also broadens the field for potential coordination and cooperation problems. From an evolutionary point of view, behavior in strategic situations was at a competitive edge. This paper demonstrates that even in games against nature, people sometimes decide as if they were in a strategic game; it outlines theoretical and empirical consequences of such a shift of the frame. It examines whether some irrationalities of human decision-making might be explained by such a shift in grasping the situation. It concludes that the mixed strategies in games against nature demand a high expertise and can only be found in situations where these strategies improve the effects of minimax-strategies that are used in cases of risk-aversion.

Success from Satisficing and Imitation: Entrepreneurs’ Location Choice and Implications of Heuristics for Local Economic Development

Decisions about location choice provide an opportunity to compare the predictions of optimization models, which require exhaustive search through very large choice sets, against the actual decision processes used by entrepreneurs choosing where to allocate investment capital. This paper presents new data on entrepreneurs’ self-described decision processes when choosing where to locate, based on scripted interviews with 49 well-placed business owners and senior managers in charge of location choice. Consideration sets are surprisingly small, especially among those who are successful. According to entrepreneurs’ own accounts, locations are frequently discovered by chance rather than systematic search. Few describe decision processes that bear any resemblance to equating marginal benefit with marginal cost as prescribed by standard optimization theory. Nearly all interviewees describe location choice decisions based on threshold conditions, providing direct evidence of satisficing rather than optimization. Imitation is beneficial for small investment projects. Decision process data collected here suggests a need to rethink standard policy tools used to stimulate local economic development.Process Model, Bounded Rationality, Interview Data, Ethnic, Discrimination, Low income, Neighborhood, Lexicographic, Non-compensatory, Business Owners

Data Understanding Applied to Optimization

The goal of this research is to explore and develop software for supporting visualization and data analysis of search and optimization. Optimization is an ever-present problem in science. The theory of NP-completeness implies that the problems can only be resolved by increasingly smarter problem specific knowledge, possibly for use in some general purpose algorithms. Visualization and data analysis offers an opportunity to accelerate our understanding of key computational bottlenecks in optimization and to automatically tune aspects of the computation for specific problems. We will prototype systems to demonstrate how data understanding can be successfully applied to problems characteristic of NASA's key science optimization tasks, such as central tasks for parallel processing, spacecraft scheduling, and data transmission from a remote satellite