CASE (Cognitive Agent for Social Environments) Department of Computer Science

1. CASE (Cognitive Agent for Social Environments) Department of Computer Science Department of Sociology/Urban Studies Trinity University

2. Outline • Cognitive Agents • Micro-macro Level Interaction • System Architecture • Decision Models • Sugarscape

3. Cognitive Agents • They have beliefs about the state of the environment. • They have knowledge about actions and plans of actions. • They have knowledge about how their actions will affect the environment and other agents. • They have explicitly goals • They are capable of reasoning about how to achieve goals (also known as intentional or deliberative agents). • They communicate using Agent Communication Language (KIF and KQML).

4. • Social iden tity Society • Bounded rationality • Social proof • Following intuition or • Social approva l d eliberative reasoning Social Laws & Emergence Constraints of properties Agents Perceptions · Communication Actions · Constraints · Environment Sphere of Influence Macro-Micro Level Interaction

5. Social Laws and Constraints • Social identity. It includes an individual’s age, gender, social positions and religions. • Social proof. It is a phenomenon that when people encounter a new situation with insufficient information, he is more likely to follow the decisions made by others, special the people from the same society as him. People are adept at adopting one others’ innovations because early decisions by group members change the environment and hence, the attractiveness of choices for subsequent group members, i.e., early group members reduce the costs for followers. • Social approval. People desire to obtain social approval from others. Others may know something that they don’t. Therefore, getting social approvals can help one share the information that others know.

6. Two Phases in a Decision Process • Tverskey and Kahneman • Editing: the agent constructs a representation of the acts, contingencies and outcomes that are relevant to the decision. • Framing: the agent frame an outcome or transaction in its mind and the utility it expects to receive. • Anchoring: the agent’s tendency to overly or heavily rely on one trait or piece of information when making decisions. • Accessibility: the importance of a fact within the selective attention. • Evaluation: the agent assesses the value of each alternative and chooses the alternative of highest value. • Two mode systems: intuition and deliberation. • Satisfying theory: being good enough.

7. Editing Agent Framing Anchoring Accessibility . . . KB Social Laws & Constraints Tone of decision-making Evaluation Perception System Architecture Two Modes of Function Intuition Deliberation communication Alternatives Satisficing Decision-Making message action Visualization Interface Environment

8. Agent Execution Function /*The function is executed independently by each agent, denoted self below.*/ function update(KBself, env, messageQueue, t) inputs: KBself, the knowledge base for agent self env, the environment messageQueue, the message queue for self t, the current step //editing phase observation(env); check(messageQueue); editing(KBself); //evaluation phase action = evaluate(KBself); message = evaluate(KBself); //performing the outputs of the evaluation phase do(action); resource-sych(env); update(env); add(message, messageQueue); masterserver-sych; // move to next step t++;

9. Decision Models • Normative model: what people should ideally do. • The attractiveness (or utility) of possible consequence of the alternative (i.e. aspects). • The probability of each consequence. • e.g. MEU • Descriptive model: what people do • e.g. MADM • Perspective model: what people should do

10. Multiple Attribute Decision-Making • MADM refers to making preference decisions over the available alternatives that are characterized by multiple, usually conflicting, attributes. • Alternatives • Attributes • Attribute Weights • Decision Matrix • Xij indicating the performance rating of the ith alternative with respect to the jth attribute.

11. An Example: Budget Reduction Decision • In 1988, a significant budget reduction at the University of Wyoming left the Athletic Department nearly $700,000 short on operating funds. qualitative quantitative Heterogeneous data type

12. Process • Attribute generation • Derive the attributes hierarchically from a super goal. • Complete and exhaustive: all important attributes • Mutually exclusive • Attribute weighting • w=(w1, …wj…, wn) s.t. wj=1 • Quantification of qualitative ratings • Normalization of attribute ratings

13. Methods • Non-compensatory rules: trade-offs among attribute values are not permitted. • Simplicity. • Do not always yield a unique solution. • Using these rules implies a risk of neglecting important information. • Compensatory rules: otherwise. • They can, theoretically, be used in all situations. • Complex value judgments • They require comparisons of attractiveness values across different attributes whereas the non-compensatory rules only require comparisons within an attribute. • The overview problem • The decisions may be difficult to justify when there is a large amount of attributes being taken into account. • Lack of concreteness • The overall attractiveness measures tell us little about the underlying pattern of attractiveness values. • The give up problem. • Compensatory rules emphasize that one has to give up certain good things to get other good things and people hate the thought of giving up anything.

14. Examples of Decision Rules Dominance Satisfying Sequential Elimination Attitude Oriented

15. Preference ordering • Ordinal relationship: which is superior to which • Preference • Attributes are preferentially independent of others • Attributes are preferentially dependent of others • Cardinal relationship: how much • Utility

16. CP-Nets • A CP-net is weak (only representing preference). • Richer models • TCP : tradeoff CP (adding importance to CP-nets). • UCP: utility CP (adding utility to CP-nets). • Uncertainty (probability)? Still research now. • Or mixing them all. !ab!c a>!a A !abc a b>!b !a !b>b B !a!b!c !a!bc b c>!c !b !c>c a!b!c a!bc C ab!c abc

17. TCP-Nets A a > !a unconditional importance relation E e > !e a b > !b !a !b > b B b d > !d !b !d > d b c > !c !b !c > c D C conditional importance relation be C > D !be D > C b!e D > C

18. UCP-Nets b > !b a !a 5 2 b !b 5 2 a > !a A B A B ab c > !c a!b !c > c !ab !c > c !a!b c > !c c > !c ab .6 .1 a!b .2 .8 !ab .1 .8 !a!b .9 .3 C C D D c d > !d !c !d > d d !d c .9 .8 !c .2 .3 u(a, b, !c, !d) = f1(a) + f2(b) + f3(a, b, !c) + f4(!c, !d) = 5 + 5 + .1 + .3 = 10.4

19. Sugarscape • Simple agents in a simple landscape create an economy. • Heterogeneous agents + physical landscape • Emergence of groups. • The rich get richer. • 1 + Random Genetic endowments • A skewed wealth distribution. • 1 + 2 + sex • Least fit members died off; the most fit members had more and more offspring. • Population swings.