Evolution, Organization and Economic Behavior

Evolution, Organization and Economic Behavior

Edited by Guido Buenstorf

This new and original collection of papers focuses on the intersection of three strands of research: evolutionary economics, behavioral economics, and management studies. Combining theoretical and empirical contributions, the expert contributors demonstrate that the intersection of these fields provides a rich source of opportunities enabling researchers to find more satisfactory answers to questions that (not only evolutionary) economists have long been tackling. Topics discussed include individual agents and their interactions; the behavior and development of firm organizations; and evolving firms and their broader implications for the development of regions and entire economies.

Chapter 2: To Weigh or Not to Weigh that is the Question: Advice on Weighing Goods in a Boundedly Rational Way

Werner Güth and Hartmut Kliemt

Subjects: economics and finance, behavioural and experimental economics, evolutionary economics


2. To weigh or not to weigh, that is the question: advice on weighing goods in a boundedly rational way Werner Güth and Hartmut Kliemt CONCEPTUALIZING PURPOSEFUL ACTION UNDER UNCERTAINTY 1 Proverbially, except for death and taxes, nothing is certain in this world. More technically speaking, since control over consequences is always imperfect, a choice must be represented formally as the problem of selecting a function that maps states of the world into a list of possible consequences. What actors expect to emerge under their choices is, of course, dependent on their knowledge and information. Their preferences over the set of functions depend on their desires as well. The desires along with beliefs determine how they will rank lists (functions) over states of the world regarded as possible. Classical decision theory assumes that the beliefs of the individual decision maker will determine probabilities pj $ 0 for all states j such that p1 1 . . . 1 pn 5 1 results. Using p 5 (p1, p2, . . ., pn) to denote the vector of probabilities for lists E 5 (E1, E2, . . ., En) , we can combine probabilities and lists: E/p 5 (E1 /p1, . . ., En /pn) ; where, of course, ‘Ej /pj’ is to be read as ‘result Ej with probability pj’. Finally, provided that certain axioms are fulfilled, a utility index u and probabilities p exist such that individual preferences over any two lists E 5 (E1, E2, . . ., En) , K 5 (K1, K2, . . ., Kn) will yield: u (E/p) $ u (K/p) iff K/p is not preferred to...

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information