Bidding for Contract Games Applying Game Theory to Analyze First Price Sealed Bid Auctions

Abstract

This study analyzed the first price sealed bid auction (FPSBA) using computer simulations. The first price sealed bid auction is a static Bayesian game with incomplete information. These games have a well defined symmetric Bayesian Nash equilibrium. The existence of the equilibrium makes it possible to find the bidders' equilibrium strategies. The equilibrium strategy maximizes the bidders' profit. This thesis assumes, (1) the bidders act rationally and have private information about their production cost, (2) the bidders' preferences and information are symmetric, (3) the buyer is committed not to deviate from the auction rules, even if a deviation would be profitable. Considering these assumptions and the equilibrium strategy, this Thesis constructed a FPSBA model. The model was transformed into an algorithm and coded in Visual Basic language. The code was used to simulate the FPSBA in different scenarios. The simulation showed the bidders' behavior and identified factors affecting the bidders' decision during bid preparation. Critical factors include the cost distribution and number of bidders. The concluding chapter presents the analytical results.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1997
Accession Number
ADA331673

Entities

People

  • Andras I. Kucama

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Basic Programming Language
  • Computer Programs
  • Computer Simulations
  • Computers
  • Contracts
  • Differential Equations
  • Game Theory
  • Government Procurement
  • Governments
  • Mathematical Models
  • Probability Density Functions
  • Probability Distributions
  • Procurement
  • Random Variables
  • Simulations
  • Spreadsheet Software
  • Statistical Analysis

Fields of Study

  • Economics

Readers

  • Computational Modeling and Simulation
  • Game Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference