Scalable Decision Making: Uncertainty, Imperfection, Deliberation (SCALE)
a workshop in conjunction with
the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2013)
September 23, 2013, Prague
Machine learning (ML) and knowledge discovery both use and serve to decision making (DM), which has to cope with uncertainty, incomplete knowledge, problem and data complexity and imperfection (limited cognitive and evaluating capabilities) of the involved heterogeneous multiple participants (aka agents, decision makers, components, controllers, classifiers, etc.). Contemporary DM deals with complex systems characterised by heterogeneous components and their goal-motivated dynamic interactions. The individual participants are selfish, i.e. follow their individual goals. There is no well-justified way how to influence or describe the resulting collective behaviour of such a system via a well-proved combination of the selfish components. Economic and natural sciences describe concepts governing the functioning of systems of selfish participants as well as ways influencing their behaviour. However, the majority of solutions rely on the human moderator/manager controlling such a system. Sophisticated ML and AI solutions developed consider artificial moderators (for instance, automatic traders used in markets, e-democracy support) as well.
Without moderator, decision making with imperfect selfish decision makers lacks a firm prescriptive basis. This problem emerges repeatedly and has no easy solution. While the theoretical, algorithmic and application achievements are immense, real-life complex problems uncover discrepancies between normative and descriptive theories. This clearly indicates the need for alternative ways, deepening and unifying the current achievements across scientific schools as well as research domains. For instance, i) the consistent theory of incomplete Bayesian games cannot be applied by imperfect participants; ii) a desirable incorporation of “deliberation effort” into the design of decision-making strategies remains unsolved. At the same time real societal, biological, economical systems efficiently cope with the imperfectness as confirmed by numerous descriptive studies. Driven by complexity, these systems exhibit a kind of (self-organising) behaviour without any intrinsic utility. This can be viewed as a result of an external control towards a common goal.
The workshop generally aims to exploit the knowledge and experience of multi-disciplinary scientific community and to extract a set of fundamental concepts describing a phenomenon of dynamic decision making with interacting imperfect selfish participants. Devices (e.g. robots), computer algorithms (e.g. controllers), humans (e.g. experts) and their combination will be considered.
The previous workshops held in conjunction with the annual Conference on Neural Information Processing Systems (NIPS) brought together a number of experts form diverse fields and we aim to attract a similar breadth of interesting contributions this year. Similarly to the preceding workshops, the selected contributions will be published by Springer.