Tutorial: Information Productivity Tutorial

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Welcome to the Tutorial on Information and Productivity.

Note: in order to run the dynamic simulations referred to in this tutorial, go here, to the Java Applet version. You will be directed to download the latest version of the Java plug-in.

"Why Information Should Influence Productivity," a survey paper by Marshall Van Alstyne and Nathaniel Bulkley, explores the connection between information transfer and productivity in firms and markets. To do so, the paper draws on economic and computational perspectives, in an effort to present a complete and comprehensive view of the issue. In this tutorial, we provide simulations that demonstrate its crucial points. Although our focus is on theoretical models as opposed to real-life findings, we make an effort to provide observational evidence that supports our abstractions.


  1. Introduction
    1. Complexity Of Organizational Bodies
    2. Harnessing The Power Of IT
    3. The Value Of Theoretical Frameworks
    4. Good Theories Fit In With Empirical Data
    5. Key Perspectives: Economics and Computational Science
    6. Informational Flow and Network Topologies
  2. Economic Perspectives I
    1. Introducing Economic Perspectives
    2. Information That Reduces Waste
    3. Better Performance Via Bayesian Updating
    4. Evolution Of Informed Agent's Belief
    5. Effect Of Altering Simulation Parameters
    6. Conclusion
  3. Economic Perspectives II
    1. Information As An Inducer Of Risk-Neutral Behavior
    2. Risk-Averse and Risk-Loving Agents
    3. Importance Of Risk-Neutrality In Agent Behavior
    4. Conclusion
  4. Economic Perspectives III
    1. Centralized Vs. Decentralized Decision Making
    2. Benign Environment Promotes Decentralized Organization
    3. Conclusion
  5. Economic Perspectives IV
    1. Relative Vs. Absolute Incentives
    2. A Simple Simulation
    3. Introducing "Absolute Incentives" Parameter
    4. Optimal Incentives Depend on Connection Density
  6. Computational Perspectives I
    1. Introducing Computational Perspectives
    2. New Information Extends The Efficient Frontier
    3. Searching For Optimal Paths
    4. Conclusion
  7. Computational Perspectives II
    1. Optimal Sharing At Partial Information Overlap
    2. Importance Of Understanding Between Teacher And Student
    3. Learner Should Connect To Translator First
    4. Conclusion
  8. Computational Perspectives III
    1. Advantages of Modular Designs
    2. A Randomly Connected System
    3. Altering Network Connections
    4. A Modular Design
    5. Conclusion
  9. Computational Perspectives IV
    1. Information Can Reduce Number Of Bad Handoffs
    2. Market Supply Chain And Bullwhip Effect
    3. Varying Consumer Demand
    4. Adding Informational Links
    5. Conclusion
  10. Information Flows and Networks I
    1. Search and Optimal Stopping
    2. Manager Interviewing a Pool of Candidates
    3. Predicting The Optimal Stopping Point
    4. Two-Step Search and Option Value
    5. Altering Probability Distributions
    6. Altering Cost and Discount Parameters
    7. Conclusion
  11. Information Flows and Networks II
    1. Ignorance Vs. Overload
    2. Changing The Informational Scope
    3. Optimal Scope Is Mid-Range
    4. Conclusion
  12. Information Flows and Networks III
    1. Topology Of Communicational Networks
    2. Informational Choking In A Hierarchy
    3. Adding Four Random Links
    4. A Better Way To Add The Connections
    5. Arbitrary Network Modification
  13. Closing Remarks
  14. Mathematical Appendices
    1. Mathematical Appendix for Topic 5
    2. Mathematical Appendix for Topic 7
    3. Mathematical Appendix for Topic 10

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