10.25728/UBS.2017.68.5
Белов Михаил Валентинович
Компания ИБС
Новиков Дмитрий Александрович
ФГБУН Институт проблем управления им. В.А. Трапезникова РАН
Модели адаптации в динамических контрактах в условиях вероятностной неопределенности
Models of adaptation in dynamic contracts under stochastic uncertainty
Управление большими системами: сборник трудов
2017
теория контрактов
задача стимулирования
вероятностная неопределенность
адаптивное поведение
задача о разладке
contract theory
incentive problem
stochastic uncertainty
adaptive behavior
ru
2017-07-31
Journal Article
http://ubs.mtas.ru/archive/search_results_new.php?publication_id=21631
1819-2440
Получены новые достаточные условия оптимальности скачкообразных и компенсаторных систем стимулирования в вероятностных задачах стимулирования; предложены и исследованы динамические модели адаптации участников организационной системы к изменению статистических характеристик внешней среды.
This work synthesizes the ideas of organization systems control theory and contract theory in the case of stochastic uncertainty repeated in time. Results on optimal reward systems for different problems are systematized. New sufficient conditions are given for the optimality of lump-sum and compensative contracts under stochastic uncertainty. Dynamic models of principal’s and agents’ adaptation to the changes in the statistical characteristics of the environment are considered. A classification of dynamic (in a sense of decision taking process) models of reward is given. Contracts between shortsighted center and agents functioning under stochastic uncertainty are considered. Reaction to such uncertainity is, indeed, one of the most crucial functions of control organs, providing adaptivity of their subordinate structural elements. Perspective future venues of research are different methods of describing uncertainity influence on agents, studiyng conditions of contract modification between farsighted center and agents and “dissonance” analysis on complex multielement dynamic organization systems.
№68 (2018)