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The simulation heuristic is a psychological heuristic, or simplified mental strategy, according to which people determine the likelihood of an event based on how easy it is to picture the event mentally. Partially as a result, people experience more regret over outcomes that are easier to imagine, such as "near misses".
Simulation heuristic: A simplified mental strategy in which people determine the likelihood of an event happening based on how easy it is to mentally picture the event happening. People regret the events that are easier to imagine over the ones that would be harder to.
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).
The typical political-military simulation is a manual or computer-assisted heuristic-type model, and many research organizations and think-tanks throughout the world are involved in providing this service to governments.
The representativeness heuristic is simply described as assessing similarity of objects and organizing them based around the category prototype (e.g., like goes with like, and causes and effects should resemble each other). [2] This heuristic is used because it is an easy computation. [4]
Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; something that is contrary to what actually happened.
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. [1] In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time.
Some approaches using MP combined with metaheuristics have begun to appear regularly in the matheuristics literature. This combination can go two-ways, both in MP used to improve or design metaheuristics and in metaheuristics used for improving known MP techniques, even though the first of these two directions is by far more studied.