User:Gabrielbsousa/sandbox

 Normative Vs. Descriptive 

Original Paragraph:

Normative decision theory is concerned with identifying the best decision to make, modelling an ideal decision maker who is able to compute with perfect accuracy and is fully rational. The practical application of this prescriptive approach (how people ought to make decisions) is called decision analysis, and is aimed at finding tools, methodologies and software (decision support systems) to help people make better decisions.

In contrast, positive or descriptive decision theory is concerned with describing observed behaviors under the assumption that the decision-making agents are behaving under some consistent rules. These rules may, for instance, have a procedural framework (e.g. Amos Tversky's elimination by aspects model) or an axiomatic framework, reconciling the Von Neumann-Morgenstern axioms with behavioral violations of the expected utility hypothesis, or they may explicitly give a functional form for time-inconsistent utility functions (e.g. Laibson's quasi-hyperbolic discounting).

The prescriptions or predictions about behaviour that positive decision theory produces allow for further tests of the kind of decision-making that occurs in practice. There is a thriving dialogue with experimental economics, which uses laboratory and field experiments to evaluate and inform theory. In recent decades, there has also been increasing interest in what is sometimes called "behavioral decision theory" and this has contributed to a re-evaluation of what rational decision-making requires.

New (Added Citations):

Normative decision theory is concerned with identifying the best decisions by considering an ideal decision maker who is able to compute with perfect accuracy and is fully rational. The practical application of this prescriptive approach (how people ought to make decisions) is called decision analysis, and is aimed at finding tools, methodologies and software (decision support systems) to help people make better decisions.

In contrast, positive or descriptive decision theory is concerned with describing observed behaviors under the assumption that the decision-making agents are behaving under some consistent rules. These rules may, for instance, have a procedural framework (e.g. Amos Tversky's elimination by aspects model) or an axiomatic framework, reconciling the Von Neumann-Morgenstern axioms with behavioral violations of the expected utility hypothesis, or they may explicitly give a functional form for time-inconsistent utility functions (e.g. Laibson's quasi-hyperbolic discounting).

The prescriptions or predictions about behaviour that positive decision theory produces allow for further tests of the kind of decision-making that occurs in practice. There is a thriving dialogue with experimental economics, which uses laboratory and field experiments to evaluate and inform theory. In recent decades, there has also been increasing interest in what is sometimes called "behavioral decision theory" and this has contributed to a re-evaluation of what rational decision-making requires.

 Heuristics Section 

Original Paragraph:

The heuristic approach to decision-making makes decisions based on routine thinking, which, while quicker than step-by-step processing, opens the risk of introducing inaccuracies, mistakes and fallacies, which may be easily disproved in a step-by-step process of thinking. One example of common and incorrect thought process is the gambler's fallacy, or believing that a random event is affected by previous random events (truth is, there is a fifty percent chance of a coin landing on heads even after a long sequence of tails). Another example is that decision-makers may be biased towards preferring moderate alternatives to extreme ones; the "Compromise Effect" operates under a mindset driven by the belief that the most moderate option, amid extremes, carries the most benefits from each extreme.

New:

Heuristics in decision-making is the ability of making decisions based on unjustified or routine thinking. While quicker than step-by-step processing, heuristic thinking is also more likely to experience fallacies or inaccuracies. The main use for heuristics in our daily routines is to decrease the amount of evaluative thinking we perform when making simple decisions, and instead make them based on unconscious rules and focusing on some aspects of the decision, while ignoring others. One example of common and erroneous thought process that arrises through heuristic thinking is the Gambler's Fallacy. Believing that a isolated random event is affected by previous isolated random events, for example if a coin is flipped to tails for a couple of turns it still carries the same probability of doing so, however intuitively it sounds more likely for it to roll heads soon. This happens because, due to routine thinking, one disregards the probability and concentrate on the ratio of the outcome, meaning, in the long run the ratio of flips should be half for each outcome. Another example is that decision-makers may be biased towards preferring moderate alternatives to extreme ones; the "Compromise Effect" operates under a mindset that the most moderate option carries the most benefit. In an incomplete information scenario, like in most daily decisions, the moderate option will look more appealing than either extreme independent of the context, based only on the fact that it gathers characteristics that can be found in either extremes.