Rational decision makers
The question ‘Are people rational decision makers?’ does not elicit a straight-forward response of yes or no. To answer this question, the various components must first be broken down and defined. Following that, the assorted strategies people utilise in decision making will be discussed. Subsequently a rational decision making model will be outlined and a brief critique will be given.
In comparision to animals, humans have remarkably well developed cognitive abilitites. Capable of language, equipped with excellent communication systems, and highly adaptive thought patterns and problem solving skills; people have moulded the environment in order to most suit and benefit mankind. Major breakthroughs and advancements in science and technology provide strong grounds for the premise that people possess an advanced intelligence; yet, does any of this actually indicate that people are rational decision makers? Psychological research suggests, to the contrary, that people are, for the most part, irrational. Much psychological research (Kahneman et. al., 1982, Baron, 1988) conveys the many mistakes and biases humans make in their choices and reasoning. Additionally, there is a plethora of evidence in daily life which adequately illustrates people’s irrationality, for example drink driving, smoking, the gambler’s fallacy. How do humans achieve so many things yet still appear so irrational in many behaviours and experimental tasks?
In order to best answer the question, ‘Are people rational decision makers?’, the various components of it first need to be broken down. The word decision is defined in the Merriam-Webster dictionary as the determination arrived at following consideration. The word rational is defined in numerous ways. It is defined in the Merrian-Webster dictionary as having reason or understanding, or, as related to, based on, or agreeable to reason. Evans and Over (1996) put forward a solution to this puzzling contradiction of mankind’s intelligence and irrationality. They suggested that the word rationality has two distinctively different meanings, and refer to these as rationality1 and rationality2. Rationality1 implies rationality on a personal level, and is shared by both animals and humans. It is used when people fulfil the base objectives of mere survival and communication. It covers the use of cognitive processes such as memory, perception, problem solving, learning and processes of adaptive and effective thinking (Evans and Over, 1996). Rationality2 denotes rationality on an impersonal level. It relies on actions derived from logical or hypothetical thought, and is restricted to humans only. Research in laboratories use theories based on logic and probability to investigate rationality2, and results indicate that people have trouble in processing that kind of information. The average person is alleged to be methodically incorrect in deductive reasoning and judging probabilities (Evans and Over, 1996), while according to Kahneman and Tversky (1972), “man is not a reasonable statistician and cannot handle probabilities or making predictions”. Following from this, people can be found irrational2 in a laboratory situation while still being rational1 in general.
One of the most famous social scientists of the 20th century, Herbert Simon (1955), posited that a human’s capability to work through and assess numerous options limits their capacity to make rational decisions. People are inclined to look at only a few parts of the available alternatives, as it is quite hard to concurrently consider every available option. Because of this the most suitable decision is not always made. There are various theories which have been developed regarding humans and rational decision making. Classical decision theory was mostly developed within the first sixty years of the 20th century. This focuses on how humans can most easily attain their desires with regard to their beliefs. Decisions can occur under varying circumstances; certainty being one, with a completely assured end result. People can be involved in making choices about preferences. Some decisions require people to take risks, where there are alternate unknown outcomes.
When making decisions with regard to preferences, people may use additive or elimination strategies. An additive strategy involves a person listing the various aspects of each component of the decision. These are then valued according to significance. The values are then summed up, and depending on the result a decision can be made. This strategy can be made more useful by placing a weight on the attributes depending on their importance. For example, imagine that someone is trying to choose between renting two apartments. Perhaps the attributes being considered are rent, noise level, cleanliness and distance to college. For each apartment, the attributes can be rated on a scale of -3 to +3. The ratings are then summed up and the apartment with the highest score is then selected. If one attribute is more important than another; if noise level is twice as important as anything else, this can be given a weight of 2 and calculated accordingly.
Another way of making decisions about preferences is elimination by aspects; gradual elimination of the less attractive alternatives(Tversky, 1972).It works on the assumption that alternatives are eliminated by examining them on each attribute in turn. For example when deciding between various apartments, the person can have a minimum criterion for each element which is important. The criteria could be to live within 2 miles of college, rent being less than E400, and adequately clean housemates to live with. Each apartment is then considered according to these minimum criteria, beginning with the most important one. An apartment that doesn’t match the criteria is eliminated, and thus a decision is made. Both of these strategies have their advantages, and research shows that people generally adapt their method according to the situation. Additive strategies are more commonly used in simpler situations while more complex decisions require a simpler approach such as elimination by strategies(Payne and Bettman, 2004 as cited in Weiten, 2010).
When people are making decisions which are risky and the outcome is unknown, they judge the probability of the outcome by calculating the expected value, using heuristics and estimating subjective utility. Expected values are often used in situations when comparing a figure of money to be invested versus the potential payoff, for example in gambling, buying lotto tickets and so on(Eysenck and Keane, 2005). For example take a national lottery. If the typical prize is 3 million euro and the chance of winning is one in 7.5 million, then the expected value of the lotto ticket is about 40 cents. This is deduced by dividing the possible outcome by the chance of winning. If a lotto ticket costs at least one euro, then this is concluded to be a poor risk to take as the ticket costs more than twice as much as its expected value. So according to this strategy, the risk is only justified if the expected value is the same as or more than the required expense. However, people still decide to do something even when the expected value is negative, as evidently the number of people buying lotto tickets shows. This is due to the fact that some people make decisions by estimating subjective utility, or to put it simply by the personal value of a decisions result, (Eysenck and Keane, 2005), regardless of how far fetched this result may be. That is to say people still buy lotto tickets as it allows them to dream of things they have always wanted.
A heuristic is a rule of thumb; a method often used to quickly come to a solution deemed to be the most optimum(Eysenck and Keane, 2005). An availability heuristic is where a decision is made based on how easily relevant occurrences of an event can be recollected. That is if someone can quickly remember certain events occurring, they believe it is more likely to occur again. For instance if someone has numerous friends who have won raffles before, they will think it more likely that they will win the raffle. A representative heuristic is when people judge the likelihood of something by how typically it occurs. The danger with this is that people assume similarity in one aspect leads to similarity in other aspects, which is not necessarily the case. Take a guy from the States, with a leather jacket, tattoos and sunglasses. If someone is then asked whether it’s more likely this man is a car owner or motorbike owner and the representative heuristic strategy is used, then the person will probably decide this description matches that of a motorcycle owner. However when using this method, the base rate is often ignored. This refers to the relative frequency of an event within a population (Eysenck and Keane, 2005). So with regard to the previous example, people judged the guy with the tattoos to be more likely to own a motorcycle yet they didn’t take the base rate into account. Since there are far more car owners than bike owners in the states it is actually more likely that the guy owns a car, rather than a bike. The representative heuristic can also explain the gamblers fallacy, the belief in runs of good luck and bad luck. “The ‘gambler’s fallacy’ is the belief that the probability of an event is lowered when that event has recently occurred, even though the probability of the event is objectively known to be independent from one trial to the next” (Clotfelter and Cook, 1993, p. 1521). Gamblers believe that a chance event is more probable if it hasn’t occurred recently. However this is a false conviction as the laws of probability only work over a number of events, they don’t apply to individual isolated events. For example someone tosses a coin and gets heads, and may then believe that a second toss will result in tails, as 50% of tosses should result in tails. However this belief is incorrect; perhaps over a number of tosses it will hold true, but the logic doesn’t apply to a single toss.
Rational decision making models require a cognitive process whereby every step naturally brings about the next one in a logical manner. Simply put, that humans make decisions based on careful thought and consideration of all the potential solutions, in order to find the best possible outcome. There are several steps in a rational decision making model. The decision need to be defined and clarified. Then identification of the most significant criteria for the process and outcome needs to take place; this leads to contemplation of all feasible results. Weighing up the consequences of these results against the probability of fulfilling the criteria then occurs. The final step is picking the best, most viable option (Simon, 1955). Rational decision making models like this work on the presupposition that that there is actually one final, optimum result. Striving towards perfection in this way can often result in a delay with decision making.
Models such as this assume it’s possible to examine all potential options, and to be aware of all the future consequences of each individual option. In theory the models are viable, however in the real world, it is not always the case that every step may be adhered to, or that all options will be open to consideration. These models are also limited by the cognitive abilities of the individual making the choice; such as how well a person’s memory functions, or the scope of their imagination. The criteria to be considered are also going to be subjective, and may be difficult to compare. These models demand a large amount of time and a lot of information. The models also attempt to counteract the function that emotions have in decision making. So again, in theory the models are feasible, however in practice maybe not so successful in every decision ever made.
So are people rational decision makers? This is not a question which can be answered with a yes or no. Primarily it depends on how rationality is defined, and what exactly a rational decision may be claimed as being. There are various factors which affect a person’s decision making process. Therefore a person’s rationality with regard to decision making is highly dependent on the situation at hand, the emotional state and cognitive capacity of the individual, and the strategies which are used to reach a result.
Baron, J. (1998). Thinking and deciding. Cambridge: Cambridge University Press.
Clotfelter, C. & Cook, P. (1993). The "Gambler's Fallacy" in Lottery Play. Management Science. Vol. 39, No. 12 pp. 1521-1525
Evans, J. & Over, D. (1997). Are people rational? Yes, no and sometimes. The Psychologist, 10(9), 403-406.
Eysenck, M. & Keane, M. (2005). Cognitive Psychology: A Student's Handbook. East Sussex: Psychology Press.
Kahneman, D. & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430-454.
Kahneman, D., Slovic, P & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.
Simon, H. (1955). A Behavioural Model of Rational Choice. The Quarterly Journal of Economics, Vol. 69, No. 1, pp. 99-118.
Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79, 281-299.
Weiten, W. (2010). Psychology: themes and variations, Eighth edition. Wadsworth: Canada.
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