Table Of ContentInterdisciplinary Studies in Economics
and Management
Vol. 3
Edited by the
lubilaumsstiftung der
Wirtschaftsuniversitat Wien
Herbert Dawid, Karl Doerner,
Georg Dorffner, Thomas F ent,
Markus Feurstein, Richard HartI,
Andreas Mild, Martin Na Uer,
Marc Reimann, Alfred Taudes
Quantitative Models of
Leaming Organizations
Springer-Verlag Wien GmbH
Prof. Dr. Herbert Dawid
Department of Economics, University of Southern California
Los Angeles, CA, USA
Dr. Karl Doemer
Prof. Dr. Richard F. HartI
Mag. Mare Reimann
Department of Production and Operations Management, University of Vienna
Vienna, Austria
Prof. Dr. Georg Dorffner
Department of Medical Cybemetics and Artificial Intelligence, University of Vienna
Vienna, Austria
Dr. Thomas Fent
Institute for Demography, Austrian Academy of Sciences
Vienna, Austria
Dr.Dr. Markus Feurstein
Dr. Andreas Mild
Prof. Dr. Martin Natter
Prof. Dr. Alfred Taudes
Department of Production Management,
Vienna University of Economics and Business Administration
Vienna, Austria
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© 2002 Springer-Verlag Wien
Originally published by Springer-Verlag Wienin2002
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ISSN 1615-7362
ISBN 978-3-7091-7280-3 ISBN 978-3-9071-6158-6 (eBook)
DOI 10.1007/978-3-7091-6158-6
Acknowledgments, aims and scope of this
research report
The Interdisciplinary Studies in Economics and Management series ofbooks
is targeted to a multidisciplinary readership. It reports about the results
generated in a joint effort of the group of researchers in mathematics and
management science that joined in the Joint Research Program (Spezial
forschungsbereich) on 'Adaptive Systems and Modeling in Economics and
ManagementScience'funded bytheAustrianScienceFoundationundergrant
SFB 010/1997-2000. The aim ofthis series is to report about the joint find
ings of this group in a manner that is both interesting for readers with a
background in economics / management and mathematics and statistics in a
way that allows also non-expert readers to grasp the ideas of modern man
agement science. Followingthe interdisciplinarydialogue that has been going
on between the researchers both aspects are covered in an integrated way,
hopefully providing a better access to modern topics in Management.
Two of the five Research Initiatives involved in the Joint Research Pro
gram (Spezialforschungsbereich) on 'Adaptive Systems andModeling in Eco
nomics and Management Science' have contributed to this collection offind
ings on organizationallearning: (#1) Computational Intelligence (headed by
Georg Doffner)and (#6) Artificial Factory (headed by Alfred Taudes).
The Jubiliiumsstijtung der Wirtschajtsuniversitiit Wien, founded
in 1997 to mark the 100th Anniversary of the WU Wien, is particularly
engaged in fostering all types of research crossing disciplinary borders. By
initiating a new series of publications devoted to these principles the WU
Jubiliiumsstijtung wants to set an example that a major investment into
an interdisciplinary style ofresearch pays off. The authors left no stone un
turned to fulfill this 'benchmark' function.
A. Taudes, November 2001
Table of Contents
Part I. Organizational Learning - Foundations and Significance
by A. Taudes
1. Microeconomics and Business Strategy ....... 9
1.1 The Classical Treatment ofKnowledge. ................... 9
1.2 Significance ofKnowledge .. ............................. 10
2. The Learning Firm....................................... 13
2.1 Competence and Knowledge .. " ,.... .. ....... .. 13
2.2 Models of Organizational Learning. ....................... 14
2.3 Outline ofthe Book '................. 14
Part II. Genetics-based machine learning applications to product
placement
by T. Fent
3. Genetic-based machine learning. .. .. .. .. .. .... .... .. ..... 23
3.1 Classifier systems. ........... ........................... 23
3.1.1 The rule base ......... ........................... 23
3.1.2 Choosing an action. .............................. 24
3.1.3 Coupled rules. ................................... 25
3.1.4 Apportionment of credit. .......................... 27
3.1.5 The bucket brigade algorithm. ..................... 27
3.1.6 Learning better rules 29
3.2 Genetic algorithms 29
3.2.1 Selection........................................ 30
3.2.2 Crossover....................................... 32
3.2.3 Mutation........................................ 35
3.2.4 Termination..................................... 37
3.3 Opportunities and limitations ............................ 37
3.3.1 Gaining knowledge 37
3.3.2 Search Strategies. ................................ 44
4 'fable of Uontents
3.3.3 Learning methods ................................ 45
4. Product Placement 47
4.1 A simple model. ....................................... 48
4.1.1 The market mechanism.. .... .. ....... ... .. ... .. .. 48
4.1.2 The buying agents. ............................... 49
4.1.3 The selling agents ................................ 49
4.2 Simulation results ................................ ...... 51
4.3 An extended model ......................... ............ 54
Part III. Ant based computation in the Management Sciences
by H. Dawid, K. Doerner, R. Hartl, M. Reimann
5. Ant Systems to solve operational problems.. .. .. .... ..... 65
5.1 Introduction to Ant Systems ,......... 65
5.2 Description ofthe problem and model formulation. ......... 66
5.2.1 Problem description 67
5.2.2 Graph-based model formulation. ......... .......... 68
5.3 Heuristic solution procedures. ............................ 69
5.3.1 Construction ofa feasible solution. ................. 69
5.3.2 Visibility........................................ 70
5.3.3 Pheromone information .... ....................... 71
5.3.4 Decision rule. .................................... 71
5.3.5 Trail update. .................................... 72
5.3.6 Post-Optimization................................ 72
5.3.7 The Ant Colony Algorithm , 73
5.4 Numerical analysis. ..................................... 73
5.5 Conclusions and future research ................. ......... 82
6. Ant based approaches to strategy. . .......... ...... ....... 83
6.1 To innovate or not to innovate .. ......................... 84
6.2 The Model ,........ .. .. .. .... ..... 85
6.3 Simulation results .................................. 87
6.4 Discussion and Conclusions .............................. 91
Part IV. Neural Network and Game Theory Applications to New
Product Development
by G. Dorffner, M. Feurstein, A. Mild, M. Natter, A.
Taudes
Table ofContents 5
7. Neural Networks in Agent-Based Economic Modeling .... 97
7.1 Introduction........................................... 97
7.2 Neural networks in agent-based economic models. .......... 97
7.3 Why cognitive modeling? ................................ 98
7.4 Some uses ofneural networks 100
7.4.1 Neural networks as models for learning 100
7.4.2 Reasoning and expectations 101
7.4.3 Clustering as simple concept formation 101
7.4.4 Tacit and explicit knowledge " 102
7.4.5 Bounded rationality 102
7.5 An example: A learning production agent. ................. 103
7.5.1 Distance ofa product to a known segment 104
7.5.2 Segment size 105
7.6 Discussion and conclusion 107
8. Organizing Learning and Product/Process Development .. 113
8.1 Organizational Structures 113
8.2 The Environment 118
8.3 Agent Design 123
8.4 Organizational Design Alternatives 125
8.4.1 Sequential Search 125
8.4.2 Search Using the House of Quality 126
8.4.3 Trial & Error Search 127
8.4.4 Evaluation of New Product Candidates 128
8.5 Hypotheses and Experimental Design 130
8.6 Results 133
8.7 Conclusion 148
8.8 Incentives to Share Knowledge .. ......................... 153
8.8.1 Bargaining Model ................................ 153
8.8.2 Simulation Results 159
8.8.3 Game Theoretical Analysis 161
8.8.4 Conclusion 162
8.8.5 Appendix 163
Part I
Organizational Learning - Foundations and
Significance
by A. Taudes
1. Neoclassical Rents and Business Strategy
1.1 The Classical Treatment of Knowledge
When a firm contemplatestheintroductionofa productto a market,it hasto
assesstheeconomicimpact ofa particularproduct / organizationaldesignal
ternative. Itthus has to knowthe relationshipbetweenproduct specifications
and revenues generated as well as the relationship between business process
design and volume, quality and cost of the output generated. Classical mi
croeconomics is based on the assumption that this knowledge is available:
"The production set is taken as a primitive datum ofthe theory" (see Mas
Collel,WhinstonandGreen, 1995,p. 128)-and, inthecaseofperfectmarkets
the specification ofthe homogeneous good is given and consumer behavior is
summarized through the price delivered by the 'abstract market agent' (see
Mas-Collel, Whinston and Green, 1995,p. 621). Productionknow-howis also
assumed to be a free good, as "the free-entry long-run competitive equilib
rium is a setupin which allfirms haveaccesstothemostefficient technology"
so that, finally, all firms in the industry are the same in the sense that they
produce the same output with maximum efficiency and make no profit (see
Mas-Collel, Whinston and Green, 1995, p. 312). In the caseofmarket power,
the rules ofthe game (effects ofownand competitiveactionson revenues) are
given, same as theeffect ofdifferent product positions onrevenuesin the case
ofdifferentiated markets (see Rester, 1999, p. 112ff.), sothat the strategyof
the firm can be found through the solution ofa multi-stage game solved via
backward recursion (e.g. operative competition through price and quantity,
positioning and entry).
In this setting, rents are socially undesirable and usually caused by
industry-specific particularities of the cost function (high sunk cost, scale
effects etc.) that can be strategicallyexploitedbyincumbents (see, e.g., Mas
Collel, Whinston and Green, 1995,p. 383ff.) orimperfections onthe demand
side such as transaction costs, limited quality information or product differ
entiation. However, already Schumpeter (1926) noted that rents could also
be causedbyimperfect imitability andthat they arethe rewardfor theenter
preneurs as the 'creators ofproduction functions'. This is modelled by Lipp
man and Rumelt (1982) who show thatex-ante uncertainty ofefficiency ofa
business process design caused by causal ambiguity in productive processes
generates stable interfirm differences in profitability, an above-normal indus-
H. Dawid et al., Quantitative Models of Learning Organizations
© Springer-Verlag Wien 2002
