Trouble with my Wiki Posts

I Had difficult and frustrating time with my mid semester course work for all week. I requested help from tutor and colleagues but I did not get any reply what so ever. The only thing that I could think of is to post it on my Blog but it is really annoying to have the work done and not been able to share it with others for comments or simply be able to comment on others as well. I felt really lonely for a while! However, I managed to sort things out on Friday with David.


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Last weeks lecture on Coorporation and Coordination was very interesting and a bit difficult to grasp at first but if you manage to understand it then you get to see the interesting side of it. Nash thoery was really interesting but I just think that at certain time we are just too busy to perform such mathematical calculations in short period of time.

Expected utility theory

Chapter 7

2 - In what ways have people been observed to violate expected utility theory?


Expected utility theory suggests that rational decision makers should weight the utilities of outcomes by their probability of occurrence. According to Baron (2000), expected utility theory as descriptive model of decision making under risk and it deals with those decisions that can be seem as gamble and gamble as one knows does not indicate any knowledge of prospect, so therefore, individual’s base their decision mainly on probabilities. Expected utility theory has always been accepted as a normative model rational choice and largely applied as a descriptive model of economic behaviour and it assumes that all reasonable individual would wish to obey the axioms of the theory, and that most people actually do majority of time.


According to Baron (2000), the use of expected value as a way of deciding about the money gamble seems fairly reasonable if individuals who want to choose between two gambles and it would make more sense to take the one with higher expected value, so that their average winning will get closer to the expected value itself. Utility judgment are useful in making important decisions, however, as the expected utility theory is a normative model, this means if individuals try to calculate expected utility for every decisions they make it will mean that they would have to spend a lot of time making calculations. However, instead of doing this we should simply familiarize ourselves with more prescriptive rules of different sorts such as, moral and personal behaviour rules. Expected utility theory seems to be a useful and adequate model of risk aversion for many purposes, and it is especially attractive in lieu of an equally obedient alternative model. Rabin (2000) suggests that extremely concave expected utility may even be useful as a parsimonious tool for modelling aversion to modest scale risk.


However, many researchers criticised expected utility theory due to the fact that they believe that this is descriptive model of decision making under risk and so alternative model was created known as prospect theory. According to this theory choice among risky prospects exhibit several pervasive effects that are inconsistent with the basic tenets of utility theory. In particular an individual underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty, and this contributes to risk aversion in choices involving sure gains and to risk seeking in choices of sure loses. Overweighting of low probabilities may contribute to the attractiveness of both insurance and gamble. Hardman (2009), suggests that prospect theory can be considered as a psychological variant of subjective to expected utility theory. Many decision makers are involved in process of editing stage whereby they first structure the decision problem in such a way as to simplify sequent evaluations and choice. This is important because it allows the decision makers to determine the potential outcomes as gains and loses relative to some reference point often known as status quo and at the evaluation stage; both a value function and weighting are applied to prospect.




In conclusion, utility theory as normative model suggests that the way of achieving good decisions comes from goals. Isenberg (1989) Researches in the area of individual decision behaviour and strategic decisions, however, has indicated that individual do not always behave according to the assumptions of utility theory. That is, they do not seek to know all possible outcomes, always assign accurate probabilities to the outcomes they recognise or consistently select the best payoff from considered alternatives.






References:


Baron, J. (2000). Thinking and Deciding (3r edition). Cambridge University Press,

Hardman, D. (2009). Judgment and Decision Making: Psychological Perspectives. BPS Blackwell

Representativeness and Availability Heuristics

Chapter 3


4 – What do think are the advantages and disadvantages of using heuristics like representativeness and availability?

Heuristics as a term means a method that help solve problems. Representativeness heuristics was first conned by Kahneman and Tversky while they were conducting research on judgment. Heuristics are recognised for their fast conclusion to a decision and representativeness heuristic is very useful for everyday decision making. However, the use of representativeness heuristics as any heuristics can lead to errors and neglect of other cognitive tasks. According to Kahneman and Tversky representativeness heuristics can lead to biases by believe that causes and effects will resemble one and other.

Although, it effectiveness representativeness heuristics can violate of the fundamental properties of probabilities such as, extensionality. Let’s for example take the problem proposed by Kahneman and Tversky about LINDA, who according to the characteristics resemble a feminist. The participants were asked to evaluate the probabilities of LINDA been a bank teller, feminist or probability of her being both bank teller and feminist. According to the probability theory the probability of her being both bank teller and feminist is must been less or equal to her being feminist or bank teller. However, the participant’s judgments were that she must have been bank teller and feminist.

Apart from the violation of extensionality, representativeness heuristics also violates base-rate and conjunction fallacy and also the use of representativeness heuristics may also lead to disjunction fallacy. Probability theory tells us that disjunction of two events is at least as likely as either of the events individually. Studies suggests that even though individuals are aware that statistical mode of reasoning is appropriate, they are often unable to correctly apply this intuition and instead they use the representativeness heuristics (Hardman, 2009)



Availability Heuristics

According to Tversky (1982), availability is an ecologically valid clue for the judgment of frequency this is because in general, frequent events are easier to recall or imagine than infrequent ones (cited in Kahneman et al 1982, p. 164). Availability heuristics suggests that we use the knowledge that is readily available rather than looking at other alternatives. When it comes to availability heuristics as studies suggested imagining a situation has better results than asked to think of specific situation. However, the overreliance on availability heuristics can lead to biases such as irretrievability of instances, the effectiveness of search set and also illusory correlation. One can argue that perceived causes of imagined events that was mentioned by Tversky (1982) might have impact on probability estimates.


Conclusion, on one hand, when individuals use representativeness heuristics what they are doing in fact is changing a judgment of probability with the similarity. This however, moves from the typical answer suggested by the theory of probability. On the other hand, availability heuristics is more dependent on memory. Availability is associated with recency effect for recalls and also with familiarity to certain things and situations. Both representativeness and availability heuristics have their usefulness, however, to reduce error we should not rely on them as they might lead to errors.

Mid Semester Work

Chapter 1

6 - Compare and contrast the fast and frugal approach to judgment with the regression analysis approach.


Reasoning is one of the important ability that both humans and animals can acquire. However, this is considered being very complex and for many years it has generated numerous studies. From the minute we wake up until the minute we go to sleep we make judgments and decisions about things, peoples and situations. Gigerenzer and colleagues (1991) suggested that humans and animals make inferences about the world under limited time and knowledge. Rationality and optimality are the guiding idea of the probabilistic approach to cognition but they are not the only reasonable guiding ideas. They suggested that there are other concepts such as simplicity and frugality, which have also inspired models of cognition. Fast and frugal models are justified by their psychological plausibility and datedness to natural environment and also are easier to understand.

Gigerenzer and colleagues (1989) also suggested that these fast and frugal violate fundamental views of classical rationality; they neither look up nor integrate all information. Even though these fast and frugal heuristics are good they are however, under constant criticism for example, Hardman (2009) suggests that these fast and frugal heuristics does not tell us what is going on in the mind of decision maker during the process of judgment.

Gigerenzer et al (1996) suggested that many studies been conducted in order to test the validity of these two views by identifying conditions under which the human mind appears more rational or irrational. However, most of the works have mainly focused in simple situations, such as Bayesian inference with binary hypotheses, one single piece of binary data, and all necessary information conveniently laid out for the participant. In many real world situations, however, there are pieces of information which are not independent. Bayes’ theorem and other rational algorithms quickly become mathematically complex and computationally intractable, at least for human minds.


A number of heuristics or short cuts are used to make fast and frugal judgments such as recognition heuristics. According to the probabilistic mental model theory inferences about the unknown states of the world are based on probability cues, such as football team, universities and so on. Gigerenzer and colleagues (1996), created a scenario whereby the participants must make a choice between two alternatives, such as on a dimension of a city. The two alternatives choice tasks occur in different contexts in which inferences need to be made with limited time and knowledge. Their study of two alternative choice tasks in situations where a person has to make an inference based only on knowledge retrieved from memory, which implies that the participants need to use their declarative knowledge to perform the task.


According to Gigerenzer el at (1996), probabilistic mental model is an inductive device that uses limited knowledge to make fast references and they perform intelligent guesses about unknown features of the world, based on uncertain indicators. To make inferences about which one of the cities has higher population (a or b), then knowledge about the reference class, for example, cities in Germany could be searched. Limited knowledge indicates that the matrix of objects by cues has missing entries i.e. objects, or cues or even cue value which might be unknown.



On the other hand we have regression analysis approach, which according to studies takes more time and are cognitively more demanding. Regression analysis usually traces the distribution of the self-governing variable (y) or some characteristic of this distribution (x). However, according to Fox there are advantages of approaching regression analysis from a general perspective. On the one hand, the appreciation for the practical problems of fitting the general models implies the specification of more limiting models such as the use of the usual linear regression model with standard errors. On the other hand, modern methods of nonparametric regression, while not quite as general as models are emerging as the practical alternatives to the more traditional linear models.



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