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Title: Towards an information formalism in group decision making systems with incomplete information
Author: Kumar, Rishi Nalin
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2017
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Human democratic and economic decision making systems face unprecedented challenges in terms of the scope and scale of their application. Owing to advances in information technology, the individuals within these systems also face fundamen- tal shifts in their ability to pool, process and share decision-relevant information, which are essential in enabling the system to function as a whole. While the de- cision making systems that prevail in industry and society are now thousands of years old, recent literature has provided inspiration for new systems in the form of empirical observations concerning how various non-human species have evolved to solve various group decision making problems throughout nature. One of the main challenges in adapting lessons from these observations is the lack of formal underpinnings in existing explanatory schemes that encompass the mechanisms of observation and communication that are both essential to making these systems work in practice and fundamental to defining them in theory. In this thesis, we demonstrate that by formalising information as a property of observables, in a systems framework, one can account for the origin and nature of the observable landscape, whilst maintaining consistency with existing definitions from previously established schemes. As will be demonstrated, introducing information formalism o↵ers a new means of establishing insights into how the components of the sys- tem interact to produce the observed, often desirable, substantive results. More broadly, by applying a cross-species information-based formalism consistently to the growing conspecific empirical literature, one can develop a new explanatory theory of the substantive properties of group decision making systems with in- complete information. Such a theory could be used to apply lessons from nature’s evolved systems to the human designed systems prevailing throughout industry and society.
Supervisor: Jensen, Henrik Jeldtoft Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral