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An Introduction to Decision Theory (Cambridge Introductions - download pdf or read online

By Martin Peterson

This advent to selection idea bargains entire and obtainable discussions of decision-making below lack of information and possibility, the rules of software idea, the talk over subjective and aim chance, Bayesianism, causal selection thought, video game idea, and social selection thought. No mathematical talents are assumed, and all ideas and effects are defined in non-technical and intuitive in addition to extra formal methods. There are over a hundred workouts with strategies, and a word list of key words and ideas. An emphasis on foundational elements of normative selection concept (rather than descriptive selection conception) makes the ebook quite helpful for philosophy scholars, however it will attract readers in more than a few disciplines together with economics, psychology, political technological know-how and machine technology.

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Extra info for An Introduction to Decision Theory (Cambridge Introductions to Philosophy)

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Transformative decision rules do not directly recommend any particular act or set of acts. Instead, they transform a given formalisation of a decision problem into another by adding, deleting or modifying information in the initial formalisation. More precisely, transformative decision rules can alter the set of alternatives or the set of states of the world taken into consideration, modify the probabilities assigned to the states of the world, or modify the values assigned to the corresponding outcomes.

We have already demonstrated this by drawing a matrix and a tree visualising the same formalisation. 2 is another example of how the same information could be presented, which is more suitable to computers. e. in an ordered list of mathematical objects. The vector is comprised of three new vectors, the first of which represents acts. The second vector represents states, and the third represents outcomes defined by those acts and states. From a theoretical perspective, the problem of how to formalise decision problems is arguably more interesting than questions about how to visualise a given formalisation.

Surely, a decision maker accepting the maximin or leximin rule does not know what the actual outcome of her choice will be. However, she knows for sure what the worst possible outcome will be. This is a kind of partial certainty, which many people seem to find attractive. If one accepts the premise that the worst possible outcome should guide one’s decisions, then the maximin and leximin rules allows one to make decisions without fearing the actual outcome – the actual outcome will always be at least as good as the worst possible outcome.

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