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Heuristics and Problem Solving Approaches
for Environmental Science

Background:

theories of rationality (Gigerenzer and Todd 1999)

unlimited

limited

making inferences from limited information can either be satisficing or using fast and frugal heuristics

search techniques for problem solving approaches (Flake )

requires a stopping rule

when do you think you have enough information

judgement under uncertainty (Kahneman et al 1982)

 

ecological rationality - using decision processes that match the structure of the information

 

List of approache:

1. Recognition heuristic

making comparsions between a list of known and unknown items

selecting which city of a pair is larger even though you only know the rank order of the top five cities

Goldstein and Gigerenzer 1999
Nisbett et al 1982
2. Dimensional analysis

checking units of the equation to determine if you are missing a factor

SmidleyNurdley constant = (right_answer/wrong_answer)*your_answer
3. Searching

Description of search strategies that will be used in the thread of written assignments.

Boolean searching

Flake
4. PMI

Focusing attention in different directions to get better input.

de Bono
5. Betting on one good reason

In the case that you recognize all the choices, it sometimes work to search through the cues and take the best cue.

This works in information environments .

Gigerenzer and Goldstein 1999
6. Risk and percieved risk

Unlike many of the above examples where our recognition or simple judgements are often correct and robust, we tend to underestimate common events and overestimate rare events.

Slovic et al. 1982
7. Prisoners' dilemma

Solving a problem and making a choice.

Making a decision when other people are presented with rational choices can cause a dilemma.

The interated prisoner's dilemma and the robustness of the "tit for tat" strategy.

Flake

 

References:

Gigerenzer, Gerd, Peter M. Todd and the ABC Reseach Group (1999). Simple Heuristics That Make Us Smart. Oxford University Press. Oxford. BD 260 .G54 1999.

Gigerenzer, Gerd and Peter M. Todd (1999). Fast and Frugal Heuristics: The Adaptive Toolbox. Chapter 1 in Gigerenzer et al. [eds].

Goldstein, Daniel G. and Gerd Gigerenzer (1999). The recognition heuristic: How ignorance makes us smart. Chapter 2 in Gigerenzer et al [eds].

Gigerenzer, Gerd and Daniel G. Goldstein (1999). Betting on one good reason: The take the best heuristic. Chapter 4 in Gigerenzer et al. [eds.]

Kahneman, Daniel, Paul Slovic and Amos Tversky (1982). Judgement under uncertainty: Heuristics and biases. Cambridge University Press. BF 441 .J8 1982.

Tversky, Amos and Daniel Kahneman (1982) Judgement under uncertainty: Heuristics and biases. Chapter 1 in Kahneman et al. [eds.]

Nisbett, Richard E., Eugene Borgida, Rick Crandall, and Harvey Reed (1982) Popular induction: Information is not necessarily informative. Chapter 7 in Kahneman et al. [eds.].

Dawes, Robyn M. (1982) The robust beauty of improper linear models in decision making. Chapter 28 in Kahneman et al. [eds.]

Slovic, Paul, Baruch Fischoff and Sarah Lichtenstein (1982) Facts versus fears: Understanding percieved risk. Chapter 33 in Kahneman et al. [eds.]

de Bono

Flake

 

John Rueter
ESR221
January 17, 2002