you have to make decisions both large and small throughout every single day of your life. when faced with some decisions, you might be tempted to just flip a coin and let chance determine your fate. for some of the complex and important decisions, we are more likely to invest a lot of time, research, effort, and mental energy into coming to the right conclusion. faced with a wide variety of options at your local superstore, you decide to base your decision on price and buy the cheapest type of soap available. the single-feature approach can be effective in situations where the decision is relatively simple and you are pressed for time.
you create a list of important features that you want the camera to have, then you rate each possible option on a scale of -5 to +5. as you can imagine, however, it can be quite time-consuming and is probably not the best decision-making strategy to use if you are pressed for time. should you drive above the speed limit in order to get there on time, but risk getting a speeding ticket? in this case, you have to weigh the possibility that you might be late for your appointment against the probability that you will get a speeding ticket. for example, if you are trying to determine if you should drive over the speed limit and risk getting a ticket, you might think of how many times you have seen people getting pulled over by a police officer on a particular stretch of highway. if your prototype is that of a careless teen that drives a hot-rod car and you are a young businesswoman who drives a sedan, you might estimate that the probability of getting a speeding ticket is quite low.
a mechanistic account of such an automatic yet compensatory decision process was proposed by glöckner and colleagues in the form of the pcs model (glöckner et al., 2014). we varied (in blocks) the number of attributes (three/four/five), and we presented a large set of decision problems with randomized values (see methods). solid-lines: accuracy as a function of the number of attributes and of trial-number (in 50-trial blocks). the number of trials in which the time limit was missed was only 0.6% of all trials and the average decision time was of around 1.5 s (see also glöckner & betsch, 2008, for similar results). error bars correspond to standard errors the wadd and the ttb strategies differ in their predictions concerning rt (glöckner & betsch, 2008). 3), there are a number of reasons to suspect that these classifications are a simplification and that the participants vary in a more continual, non-dichotomous, manner. we used a job-interview framing and provided the participants with accuracy feedback.
the non-compensatory ttb heuristic is a lexicographic strategy that applies rules sequentially and neglects much of the information. information theory and an extension of the maximum likelihood principle. reasoning the fast and frugal way: models of bounded rationality. journal of experimental psychology: learning, memory, and cognition, 34(5), 1055-1075. glöckner, a. visual fixations and the computation and comparison of value in simple choice. using hierarchical bayesian methods to examine the tools of decision-making. the gut chooses faster than the mind: a latency advantage of affective over cognitive decisions.
the additive feature model. this method involves taking into account all the important features of the possible choices have equal weight (equal weight strategy) or have different weights for the attributes (weighted additive strategy). 1 waad weighted additive strategy a decision making method in which all from psych ib at the awty international, lexicographic strategy, lexicographic strategy, elimination by aspects strategy, additive strategy psychology examples, additive strategy example. additive strategies are decision making methods in which all possible options or variables are weighed or given a score (good or bad) and then compared to each other in order to make a decision. options are measured and then cumulatively weighed against each other to make a decision.
the participants were classified as users of one of three strategies: weighted additive utility (wadd), weighted additive models are well known for dealing with multiple criteria decision making problems. fuzzy goal the model assumes that the use of the rational weighted additive strategy and the boundedly rational, single feature model psychology definition, decision-making psychology theories, types of decision making in psychology, decision-making strategies psychology, elimination strategy psychology, additive model psychology definition, additive model of decision making, 4 models of consumer decision making
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