The Challenge of Flexible Intelligence for Models of Human Behavior

Game theoretic predictions about equilibrium behavior depend upon assumptions of inflexibility of belief, of accord between belief and choice, and of choice across situations that share a game-theoretic structure. However, researchers rarely possess any knowledge of the actual beliefs of subjects, and rarely compare how a subject behaves in settings that share game-theoretic structure but that differ in other respects. Our within-subject experiments utilize a belief elicitation mechanism, roughly similar to a prediction market, in a laboratory setting to identify subjects’ beliefs about other subjects’ choices and beliefs. These experiments additionally allow us to compare choices in different settings that have similar game-theoretic structure. We find first, as have others,that subjects’ choices in the Trust and related games are significantly different from the strategies that derive from subgame perfect Nash equilibrium principles. We show that, for individual subjects, there is considerable flexibility of choice and belief across similar tasks and that the relationship between belief and choice is similarly flexible. To improve our ability to predict human behavior, we must take account of the flexible nature of human belief and choice

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

A voyage to Mars: A challenge to collaboration between man and machines

A speech addressing the design of man machine systems for exploration of space beyond Earth orbit from the human factors perspective is presented. Concerns relative to the design of automated and intelligent systems for the NASA Space Exploration Initiative (SEI) missions are largely based on experiences with integrating humans and comparable systems in aviation. The history, present status, and future prospect, of human factors in machine design are discussed in relation to a manned voyage to Mars. Three different cases for design philosophy are presented. The use of simulation is discussed. Recommendations for required research are given