Skip to main content
SpringerLink
Log in

Creating a Space for Collective Problem-Solving

  • Reference work entry
  • First Online:
Encyclopedia of Social Network Analysis and Mining

  • 128 Accesses

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...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,500.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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.learningandteaching.info/learning/assimacc.htm. Accessed 1 Sept 2010

  • 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

    Google Scholar 

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

    MATH  MathSciNet  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, 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 

  • 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

    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

    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

    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 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

    Google Scholar 

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

    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 

  • 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. doi:http://dx.doi.org/10.1287/opre.34.6.871

  • 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 

  • 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

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Management Sciences Inc, Albuquerque, NM, USA

    Kshanti A. Greene

  2. Department of Computer Science, University of New Mexico, Albuquerque, NM, USA

    Joe M. Kniss & Steven S. Garcia

Authors
  1. Kshanti A. Greene
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joe M. Kniss
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven S. Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Greene, K.A., Kniss, J.M., Garcia, S.S. (2014). 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/10.1007/978-1-4614-6170-8_102001

Download citation

Publish with us

Policies and ethics

Search

Navigation