Deep complexity and the social sciences : experience, modelling and operationality
著者
書誌事項
Deep complexity and the social sciences : experience, modelling and operationality
(New horizons in institutional and evolutionary economics)
E. Elgar, c2010
大学図書館所蔵 全5件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 435-455) and index
内容説明・目次
内容説明
In this innovative work, Robert Delorme comprehensively explores uncertainty (the irreducibility to numerically measurable probabilities) and ignorance in economics, management and the social sciences through an alternative, systematically built analytical framework.This unique book takes uncertainty and ignorance seriously and addresses them as instances of ?deep complexity? (problem situations so deeply ill-structured that they cannot be grasped with the concepts and tools of classical science). Building on the works of Herbert Simon, Heinz von Foerster and John von Neumann, the author develops an alternative framework that encompasses, rather than rejects, the classical framework. The outcome of this novel approach is ?effective deep complexity?, comprising three aspects: an effective alternative framework, which brings an answer to a fundamental issue on the implications of uncertainty for scientific reasoning; a behavioural theory of deeply ill-structured problem-situations; and a decision-and-action support system.Robert Delorme has provided an invaluable resource for researchers and academics in the broad realm of economics and business management. This work will also appeal to decision-makers and policymakers due to its practical applications, including structural economic policy, transport and industry.
目次
Contents: Preface Part I: Experience 1. Heuristics 2. Originality Part II: Modelling 3. Relativity 4. Deep Complexity as Product 5. Deep Complexity as Process 6. Generality Part III: Operationality 7. Synthesis 8. Applications General Conclusion References Index
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