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Area of Science:

  • Complex adaptive systems
  • Game theory
  • Information economics

Background:

  • The prevailing assumption is that superior data provides a competitive edge.
  • Big data acquisition is a significant trend across industries.
  • Understanding the impact of information quality and quantity in competitive environments is crucial.

Purpose of the Study:

  • To investigate the relationship between data quality/quantity and competitive performance.
  • To challenge the assumption that more/better data always confers an advantage.
  • To explore the role of information asymmetry in agent-based competition.

Main Methods:

  • Modeling a complex adaptive system with competing agents.
  • Simulating competition for a limited resource.
  • Varying levels of information granularity for agents.

Main Results:

  • Agents with more and finer data performed worse in specific scenarios.
  • The impact of information asymmetry on payoffs is not linear.
  • Population composition significantly influences the effect of information advantage.

Conclusions:

  • Excessive or overly fine-grained data can be detrimental in competitive settings.
  • The value of information is context-dependent and influenced by system dynamics.
  • Information asymmetry's effect on outcomes requires nuanced analysis considering population structure.