Confirmed Invited Talks (in alphabetical order)

Daniel Braun , Max Planck Institute for Biological Cybernetics, Germany

INFORMATION-THEORETIC BOUNDED RATIONALITY FOR LEARNING AND DECISION-MAKING

We study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems and show how the formation of abstractions and decision-making hierarchies depends on information-processing costs.

Itzhak Gilboa , HEC Paris, France

RATIONALITY AND THE BAYESIAN PARADIGM

It is argued that, contrary to a rather prevalent view within economic theory, rationality does not imply Bayesianism. The note begins by defining these terms and justifying the choice of these definitions, proceeds to survey the main justification for this prevalent view, and concludes by highlighting its weaknesses.

Tom Griffiths , University Berkeley, USA

BOUNDED OPTIMALITY AND RATIONAL METAREASONING IN HUMAN COGNITION

Human decision-making is often described as irrational, being the result of applying error-prone heuristics. I will argue that this is partly a consequence of the use of an unrealistic standard of rationality, and that the notion of bounded optimality from the artificial intelligence literature provides a better framework for understanding human behaviour. Within this framework a rational agent seeks to execute the best algorithm for solving a problem, taking into account available computational resources and the cost of time. We find that several classic heuristics from the decision-making literature are bounded optimal, assuming people have access to particular computational resources. This establishes a new problem: how do people find such good heuristics? I will discuss how this problem can be addressed via rational metareasoning, which examines how rational agents should decide what algorithm to use in solving a problem. The result is a view of human decision-making in which people are intelligently and flexibly making the most of their limited computational resources.

Pedro Ortega , Google DeepMind, UK

AGENCY AND CAUSALITY IN DECISION MAKING

 We review the distinction between evidential and causal decision-making and the challenges that this distinction poses to the application of the expected utility principle. We furthermore establish firm connections between causality, information-theory, and game-theoretic concepts. Finally, we show how to use the aforementioned connections to construct adaptive agents that are universal over a given class of stochastic environments - such as Thompson sampling.

Timothy J. Pleskac, Max Planck Institute for Human Development, Germany

THE RATIONAL STATUS OF QUANTUM PROBABILITY THEORY APPLIED TO HUMAN DECISION MAKING     (joint work with Jerome R. Busemeyer)

Quantum probability theory (QPT) is a probabilistic framework, alternative to Classic Probability Theory (CPT) that has been employed to model some of the paradoxical phenomena found with human judgments and decisions. One question that arises, however, is why an agent might behave this way especially given that these judgments and decisions appear to deviate from rationality? We will argue that QPT can fulfill the requirement for the Dutch Book theorem, which has been used to justify the rational status of CPT. A second question is how these quantum processes work? We will show how the heuristic processes people use to make judgments and decisions can be modeled with quantum information theory, which perhaps paradoxically provides a better and more parsimonious description of these boundedly rational heuristic processes people use than models grounded in classic information theory. In sum, we will argue that QPT can offer a principled account of the processes people use to make judgments and decisions with their limited computational resources and those judgments and decisions can nevertheless be quite rational.

Naftali Tishby , The Hebrew University, Israel

PRINCIPLES AND ALGORITHMS FOR SELF-MOTIVATED BEHAVIOUR (joint work with Stas Tiomkin and Daniel Polani)

For planning with high uncertainty, or with too many possible end positions as in games like Go or even chess, one can almost never solve the optimal control problem and must use some receding horizon heuristics. One such heuristics is based on the idea of maximizing empowerment, namely, keep the number of possible options maximal. This has been formulated using information theoretic ideas as maximizing the information capacity between the sequence of actions and the possible state of the system atsome finite horizon, but no efficient algorithm for calculating this capacity was suggested. In this work we propose a concrete and efficient way for calculating the capacity between a sequence of actions and future states, based on local linearization of the dynamics and Gaussian channel capacity calculation. I will describe the new algorithm and some of its interesting implications.

David H. Wolpert , Santa Fe Institute, USA

WHAT THE RECENT REVOLUTION IN NETWORK CODING TELLS US ABOUT THE ORGANIZATION OF SOCIAL GROUPS (joint work with Justin Grana)

 The extended abstract of the talk can be found here

Please check  Workshop Programme for actual information.