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SCIENTIFIC NETWORKS ON DATA LANDSCAPES: QUESTION DIFFICULTY, EPISTEMIC SUCCESS, AND CONVERGENCE

Published online by Cambridge University Press:  13 November 2013

Patrick Grim ,
Daniel J. Singer ,
Steven Fisher ,
Aaron Bramson ,
William J. Berger ,
Christopher Reade ,
Carissa Flocken  and
Adam Sales
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Abstract

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes.

Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts–Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

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Type
Discussion
Information
Episteme , Volume 10 , Issue 4 , December 2013 , pp. 441 - 464
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Axelrod, R. 1984. Evolution of Cooperation. New York: Basic Books.Google Scholar
Bansal, S., Grenfell, B. T. and Meyers, L. A. 2007. ‘When Individual Behavior Matters: Homogeneous and Heterogeneous Networks in Epidemiology.’ Journal of the Royal Society Interface 4, 879–891.Google Scholar
Boccara, N., Goles, E., Martinez, S. and Picco, P. (eds). 1993. Cellular Automata and Cooperative Systems. Nato Science Series C: Mathematical and Physical Sciences Vol. 396. Dordrecht: Kluwer.Google Scholar
Cangelosi, A. and Parisi, D. (eds). 2002. Simulating the Evolution of Language. London: Springer.Google Scholar
Conklin, J. 2003. ‘Dialog Mapping: Reflections on an Industrial Strength Case Study.’ In Kirschner, P., Shum, S. J. B. and Carr, C. S. (eds), Visualizing Argumentation – Tools for Collaborative and Educational Sense-Making, pp. 117–36. London: Springer-Verlag.Google Scholar
Grim, P. 2006. Tangled Webs: The Philosophical Importance of Networks. The Marshall Weinberg Lecture, University of Michigan.Google Scholar
Grim, P. 2007. Network Structure in Cooperation, Communication, and Epistemology. Center for Philosophy of Science, University of Pittsburgh, September 2007.Google Scholar
Grim, P. 2009a. ‘Network Simulations and their Philosophical Implications: Models for Semantics, Pragmatics, and Epistemology.’ Models and Simulations 3, Charlottesville Virginia, March 2009.Google Scholar
Grim, P. 2009b. ‘Threshold Phenomena in Epistemic Networks.’ Proceedings, AAAI Fall Symposium on Complex Adaptive Systems and the Threshold Effect, FS-09-03. Menlo Park, CA: AAAI Press.Google Scholar
Grim, P., Selinger, E., Braynen, W., Rosenberger, R., Au, R., Louie, N. and Connolly, J. 2005. ‘Modeling Prejudice Reduction: Spatialized Game Theory and the Contact Hypothesis.’ Public Affairs Quarterly, 19: 95–126.Google Scholar
Grim, P., Wardach, S. and Beltrami, V. 2006. ‘Location, Location, Location: The Importance of Spatialization in Modeling Cooperation and Communication.’ Interaction Studies: Social Behavior and Communication in Biological and Artificial Systems, 7: 43–78.Google Scholar
He, J., Reeves, C., Witt, C. and Yao, X. 2007. ‘A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Realizations and Predictability.’ Evolutionary Computation, 15: 435–43.Google Scholar
Hegselmann, R. ‘Costs and Benefits of Cognitive Division of Labor and Epistemic Networking: An Agent-Based Simulation Study.’ Presentation at the Choosing the Future of Science: The Social Organization of Scientific Inquiry Conference. April 20, 2013. Pittsburgh, PA.Google Scholar
Hegselmann, R. & Flache, A. 1998. ‘Understanding Complex Social Dynamics: A Plea for Cellular Automata Based Modeling.’ Journal of Artificial Soceities and Social Simulation, 1. <http://jasss.soc.surrey.ac.uk/1/3/1.html>Google Scholar
Hegselmann, R. & Krause, U. 2006. ‘Truth and Cognitive Division of Labour: First Steps Toward a Computer Aided Social Epistemology.’ Journal of Artificial Societies and Social Simulation, 9, no. 3. http://jasss.soc.surrey.ac.uki/9/3/10.html.Google Scholar
Lazer, D. and Friedman, A. 2007. ‘The Network Structure of Exploration and Exploitation.’ Administrative Science Quarterly, 52: 667–94.Google Scholar
Meyers, L. A., Pourbohloul, B., Newman, M. E. J. and Pourbohloul, B 2006. ‘Predicting Epidemics on Directed Contact Networks.’ Journal of Theoretical Biology, 240: 400–18.Google Scholar
Naudts, B. and Kallel, L. 2000. ‘A Comparison of Predictive Measure of Problem Difficulty in Evolutionary Algorithms.’ IEEE Transactions on Evolutionary Computation, 4: 1–15.Google Scholar
Newman, M. E. J 2002. ‘The Spread of Epidemic Disease on Networks.’ Physical Review E, 66: 016 128 (doi: 10.1103/PhysRevE.66.016128).Google Scholar
Rittel, H. and Webber, M. 1973. ‘Dilemmas in a general theory of planning.’ Policy Sciences, 4: 155–69. Reprinted in Cross, N. (ed), Developments in Design Methodology, pp. 135–144. Chichester: John Wiley and Sons, 1984.Google Scholar
Schelling, T. C. 1969. ‘Models of Segregation.’ American Economic Review, 59: 488–93.Google Scholar
Schelling, T. C. 1978. Micromotives and Macrobehavior. New York: W. W. Norton.Google Scholar
Watts, D. J. and Strogatz, S. H. 1998. ‘Collective Dynamics of ‘Small-world’ Networks.’ Nature, 393: 440–2.Google Scholar
Weisberg, M. and Muldoon, R. 2009. ‘Epistemic Landscapes and the Division of Cognitive Labor.’ Philosophy of Science, 76: 225–52.Google Scholar
Zollman, K. J. 2007. ‘The Communication Structure of Epistemic Communities.’ Philosophy of Science, 74: 574–87.Google Scholar
Zollman, K. J. 2010a. ‘The Epistemic Benefit of Transient Diversity.’ Erkenntnis, 72: 17–35.Google Scholar
Zollman, K. J. 2010b. ‘Social Structure and the Effects of Conformity.’ Synthese, 172: 317–40.Google Scholar
Zollman, K. J. 2012. ‘Social Network Structure and the Achievement of Consensus.’ Politics, Philosophy & Economics, 11: 26–44.Google Scholar