The Hurwicz Criterion, presented in in 1951, is probably the earliest novel contribution to the field of economics for which Leo has been recognized. It provides a formula for balancing pessimism and optimism in decision-making under uncertainty – that is, when future conditions are to some extent unknown. A defining feature of the Hurwicz Criterion is that it allows the decision maker to simultaneously take into account both the best and the worst possible outcomes. To do this, the decision maker chooses a “coefficient of pessimism”, called alpha (α), which is a decimal number between 0 and 1. This number determines the emphasis on the worst possible outcome. Then the number (1-α) determines the emphasis to be placed on the best outcome. So, if the coefficient of pessimism is .4, then the emphasis on the best outcome will be .6. Show If alpha determines the emphasis to be placed on the best outcome, it may be called a “coefficient of optimism.” Either a “coefficient of pessimism” or a “coefficient of optimism” may also be called a “coefficient of realism.” This contrasts with other approaches, such as:
The Hurwicz Criterion is sometimes confused with Minimax Regret, which compares what I actually did with what I would have done if I could have predicted the future. Another way of putting this is that Minimax Regret looks at the maximum possible regret: the maximum difference, for each scenario, between what I actually did and what I “coulda-shoulda-woulda” done. It then takes the path that minimizes potential regret. The table below shows assumed “pay-offs” (economic results expressed in monetary units such as dollars) for building either no reservoir, a small reservoir or a large reservoir under three scenarios: low, medium and high climate-change impacts. (I got the idea for this example from Green and Weatherhead, “Coping with climate change uncertainty for adaptation planning“. However, my example is hypothetical, is mine alone, and does not make use of their data or the novel strategy that they propose.) For my hypothetical example, we’re assuming that the pay-offs are known. What isn’t known is the degree to which climate change will impact the activities, such as agriculture, that would be supported by the reservoir. The numbers in red represent the result that should be chosen under each approach. “Optimistic” in this context means “maximizing return on investment.” It does not mean a belief that “all will be well.” For instance, a large investment in insurance may give the best return in case of a catastrophe. This is, in fact, my thinking in the example below. Taking the different approaches one at a time, from right to left in the table:
Table of pay-offs for building a reservoir under differing climate scenarios with Hurwicz Criterion alpha = coefficient of pessimism The paper in which the Hurwicz Criterion was originally stated is: “The Generalised Bayes Minimax Principle: A Criterion for Decision Making Under Uncertainty,” Cowles Commission Discussion Paper 355, February 8, 1951. 7p. Is maximin criterion pessimistic?The Maximin criterion is a pessimistic approach. It suggests that the decision maker examines only the minimum payoffs of alternatives and chooses the alternative whose outcome is the least bad.
Is Maximax pessimistic?Maximin (pessimistic), which looks only at the worst possible result in each scenario, and chooses the “best of the worst”. Maximax (optimistic), which looks only at the best possible result in each scenario, and chooses the “best of the best”
What is Maximax and maximin criterion?Maximizing the payoff at all cost (risk taker) — Maximax. Maximizing the minimum payoff (risk aversion) — Maximin. Minimizing the potential regret (loss or missing out) — Minimax.
What is the maximin criterion?Maximin / Leximin Criterion
"Maximin" means "Maximize the Minimum Payoff". This criterion is appropriate for Pessimist persons. Using this criterion, the decision-maker looks at the worst that can happen under each action and then choose the action that has the largest payoff for the worst-case scenario.
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