Decision Making and Judging z Representativeness Heuristic yrule

Decision Making and Judging z. Representativeness Heuristic yrule of thumb for judging the likelihood of things in terms of how well they seem to represent, or match, particular prototypes ymay lead one to ignore other relevant information

Decision Making and Judging z. Availability Heuristic yestimating the likelihood of events based on their availability in memory yif instances come readily to mind (perhaps because of their vividness), we presume such events are common y. Example: airplane crash

Decision Making and Judging z. Overconfidence ytendency to be more confident than correct ytendency to overestimate the accuracy of one’s beliefs and judgements

Decision Making and Judging z. Framing ythe way an issue is posed yhow an issue is framed can significantly affect decisions and judgements y. Example: What is the best way to market ground beef- As 25% fat or 75% lean?

Decision Making and Judging z. Belief Bias ythe tendency for one’s preexisting beliefs to distort logical reasoning ysometimes by making invalid conclusions seem valid, or valid conclusions seem invalid z. Belief Perseverance yclinging to one’s initial conceptions after the basis on which they were formed has been discredited

Artificial Intelligence z. Artificial Intelligence ydesigning and programming computer systems xto do intelligent things xto simulate human thought processes • intuitive reasoning • learning • understanding language

Artificial Intelligence z. Artificial Intelligence yincludes practical applications xchess playing xindustrial robots xexpert systems yefforts to model human thinking inspired by our current understanding of how the brain works

Artificial Intelligence z. Neural Networks ycomputer circuits that mimic the brain’s interconnected neural cells yperforming tasks xlearning to recognize visual patterns xlearning to recognize smells