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Creating a Space for Collective Problem-Solving

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Encyclopedia of Social Network Analysis and Mining

Synonyms

Collaborative problem-solving; Collective intelligence; Group decision-making; Group problem-solving; Organismic computing

Glossary

Random variable:

A variable whose value may vary due to random behavior and hence is assigned a stochastic value

Graphical model:

A graph composed of nodes and edges, in which the nodes are typically random variables and an edge represents a direct dependency between two nodes

Topology:

The abstract structure of a graphical model, e.g., the configuration of the nodes and edges

Conditional probability:

The value of a random variable is dependent or conditioned on the value of one or more other random variables

Conditional independence:

Knowledge of the value of a random variable can make other variables independent of each other, depending on the graph topology

Parameters:

The numerical values associated with a graphical model. In most cases, this is a prior probability or a conditional probability

Problem graph:

A graphical model that contains...

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References

  • Angel E, Shreiner D (2012) Interactive computer graphics: a top-down approach using OpenGL, 6th edn. Pearson Education, Boston

    Google Scholar 

  • Atherton JS (2010) Learning and teaching; assimilation and accommodation. www.actforlibraries.org/piagets-theories-of-assimilation-accommodation-and-child-development/. Accessed June 3 2017

  • Buntine (1996) A guide to literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8(2):195–210

    Article  Google Scholar 

  • Corkill D (1991) Blackboard systems. AI Expert 6(9):40–47

    Google Scholar 

  • Coupe VM, van der Gaag LC (1998) Practicable sensitivity analysis of Bayesian belief networks. In: Prague stochastics 98, Union of Czech Mathematicians and Physicists, pp 81–86

    Google Scholar 

  • Crane R (2010)–2012 Conversations with Riley Crane (MIT Media Lab), consultant

    Google Scholar 

  • de Berg M, Cheong O, van Kreveld M, Overmars M (2008) Computational geometry: algorithms and applications, 3rd edn. TELOS, Santa Clara

    Book  MATH  Google Scholar 

  • Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271

    Article  MathSciNet  MATH  Google Scholar 

  • Friedman, Getoor, Koller and Pfeffer (1999) Learning probabilistic relational Models. In: IJCAI 16: 1300–1309

    Google Scholar 

  • Geiger D, Verma T, Pearl J (1990) Identifying independence in Bayesian networks. Networks 20:507–533

    Article  MathSciNet  MATH  Google Scholar 

  • Gershenson JK (2007) Pugh evaluation; NASA ESMD capstone design

    Google Scholar 

  • Greene K (2010) Collective belief models for representing consensus and divergence in communities of Bayesian decision-makers. PhD thesis, Department of Computer Science, University of New Mexico

    Google Scholar 

  • Greene K and Young T (2017) Method to display a graph containing nodes and edges in a two-dimensional grid, approved US patent application #15/206,241

    Google Scholar 

  • Greene K, Kniss J, Luger G (2010) Representing diversity in communities of Bayesian decision-makers. In: Proceedings of the IEEE international conference on social computing, social intelligence and networking symposium, Vancouver

    Google Scholar 

  • Heuer, R (1999) Psychology of intelligence analysis, Central Intelligence Agency

    Google Scholar 

  • Howard R, Matheson J (eds) (1984) Readings on the principles and applications of decision analysis, vol 2. Strategic Decisions Group, Menlo Park, pp 6–16

    Google Scholar 

  • Jensen F (1996) An introduction to bayesian networks. Springer, Secaucus

    Google Scholar 

  • Kindermann R, Snell JL (1980) Markov random fields and their applications. American Mathematical Society, Providence

    Book  MATH  Google Scholar 

  • Koller D, Milch B (2001) Multi-agent influence diagrams for representing and solving games. In: IJCAI, Seattle, pp 1027–1036

    Google Scholar 

  • Langley P, Rogers S (2005) An extended theory of human problem solving. In: Proceedings of the 27th annual meeting of the cognitive science society, Stresa

    Google Scholar 

  • Lee DT, Preparata FP (1984) Computational geometry: a survey. IEEE Trans Comput c-33:1072–1101

    Article  MathSciNet  Google Scholar 

  • Matzkevich I, Abramson B (1992) The topological fusion of Bayes nets. In: Proceedings of the 8th conference on uncertainty in artificial intelligence, Stanford

    Chapter  MATH  Google Scholar 

  • Milch B (2000) Probabilistic models for agents beliefs and decisions. In: Proceedings of the 16th UAI, Stanford. Morgan Kaufmann, San Francisco, pp 389–396

    Google Scholar 

  • Muller-Prothmann T (2006) Leveraging knowledge communication for innovation. PhD thesis, FU Berlin

    Google Scholar 

  • Murphy (2001) Learning Bayes net structure from sparse data sets. Technical report, UC Berkeley

    Google Scholar 

  • Murphy K (1998) A brief introduction to graphical models and bayesian networks. http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html. Accessed 11 Nov 2012

  • Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • O’Rourke J (1988) Computational geometry. Ann Rev Comput Sci 3:389–411

    Article  MathSciNet  Google Scholar 

  • Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufman, San Francisco

    MATH  Google Scholar 

  • Pearl, Verma (1991) A theory of inferred causation. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, 441–452

    Google Scholar 

  • Pennock DM, Wellman MP (1999) Graphical representations of consensus belief. In: Proceedings of the 15th conference on uncertainty in artificial intelligence, Stockholm

    Google Scholar 

  • Polya G (1945) How to solve it. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Prim RC (November 1957) Shortest connection networks and some generalizations. Bell Syst Tech J 36(6):1389–1140

    Article  Google Scholar 

  • Ritchey T (2007) Wicked problems: structuring social messes with morphological analysis. Springer, Heidelberg

    Google Scholar 

  • Shachter RD (1986) Evaluating influence diagrams. Oper Res 34(6):871–882. https://doi.org/10.1287/opre.34.6.871

    Article  MathSciNet  Google Scholar 

  • Shamos ME (1978) Computational geometry. Yale University

    Google Scholar 

  • Shultz TR, Buckingham D, Schmidt WC, Mareschal D (1995) Modeling cognitive development with a generative connectionist algorithm. In: Simon TJ, Halford GS (eds) Developing cognitive competence: new approaches to process modeling. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  • Stoer M, Wagner F (1997) A simple min-cut algorithm. J ACM 44(4):585–591

    Article  MathSciNet  MATH  Google Scholar 

  • Sueur C, Deneubourg JL, Petit O (2012) From social network (centralized vs. decentralized) to collective decision-making (unshared vs. shared consensus). PLoS One 7(2). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290558/. Accessed 11 Nov 2012

    Article  Google Scholar 

  • Szwarcberg M (2012) Revisiting clientelism: a network analysis of problem-solving networks in Argentina. Soc Netw 34(2):230–240

    Article  Google Scholar 

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Acknowledgments

Some of the described research is funded by DARPA grant #D11PC20150. Thanks to Pietro Michelucci at Strategic Analysis, Inc. for helping to make this work possible. Also thanks to Thomas Young at Social Logic Institute for his contributions to the project.

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Correspondence to Kshanti A. Greene .

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Greene, K.A., Kniss, J.M., Garcia, S.S. (2018). Creating a Space for Collective Problem-Solving. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_102001

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