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Decision Making in Complex Ecological Systems: How do we model it?

Decision Making in Complex Ecological Systems: How do we model it?. Jacopo A. Baggio jbaggio@asu.edu Center for the Study of Institutional Diversity School of Human Evolution and Social Change Arizona State University. Imagine how difficult physics would be if electrons could think .

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Decision Making in Complex Ecological Systems: How do we model it?

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  1. Decision Making in Complex Ecological Systems: How do we model it? Jacopo A. Baggio jbaggio@asu.edu Center for the Study of Institutional Diversity School of Human Evolution and Social Change Arizona State University

  2. Imagine how difficult physics would be if electrons could think. (Gell-Mann)

  3. Outline • Decision making? • Genetic Algorithm • Use of simple if/then rules • Importance of feedback loops • Imitation and Innovation and the role of networks • Use of multi-method approaches

  4. Intro • Social-ecological systems are complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales. • We need to improve our understanding of (?) • conditions under which cooperative solutions are sustained • how social actors can make robust decisions in face of uncertainty • how the topology of interactions between social and biophysical actors affect governance.

  5. How do we make decisions? • Example of decisions when managing an ecological system • What species should we monitor? • What are the effects of building a road in this area? • Should we protect this area? • At what rate should we harvest resources? • In all cases, two type information is needed and used: • Background knowledge (past monitoring strategies results, effects of fragmentation, development of the social system, history, norms and rules existing in the social system etc.) • Specific information about this case (specific report of species living in the area under study, migration rates, importance of the patches that would be fragmented etc.)

  6. Model Typologies • Inductive (based on observations, harder to generalise) • Deductive (explicit construction of socio-psychological processes) • Genetic algorithm (genetic metaphor) • Rule-based algorithm (semiotic metaphor) • Model typologies can be combined! (and they actually often are)

  7. Evolutionary models: Genetic Algorithms • Behavior evolves in order to maximize a given fitness function (often survival length). An “ideal” behavior exists.

  8. Symbolic models: Rule-based Algorithms • Behavior is based on conditions. If condition A happens, an individual has a probability p of behaving B and 1-p of behaving C • Multiple outcomes with different probabilities are possible • Multiple conditions are possible

  9. One common element: feedbacks! • Feedbacks as the “reaction” to a decision (can be positive or negative) in a literal view or in a mathematical view • What can we anticipate by knowing if a feedback loop is positive or negative? (that is, if we can)

  10. We are not alone!

  11. Modeling decision making in groups • Interaction between individuals • Structured • Imitation • Innovation • Driven by Authority • Cultural views • Individual characteristics

  12. What influences decisions? • Networks • Authority • Personal views

  13. Information/knowledge diffusion • Can be modeled as a disease spreading • An individual/organization is “infected” when receiving information/knowledge • Once infected can infect others • Spread of infection depend on: • individual’s capacity to acquire & retransfer[absorptive capacity] • presence of “special” individuals • topology of connections

  14. Basic epidemiology • Individuals in a population (N) can be: S(usceptible), I(nfected) or R(emoved) • Models     

  15. A randomized network (nullmodel) Scale-free & differentactor’s absorptivecapacities: speed = + 16% Sameactor’s absorptivecapacities: speed= + 22% Increased (3x) clustering: speed= + 52% Information diffusion Numerical simulation on the network  Highestefficiency NOT achievedimprovingindividualsbutgroups

  16. Opinion diffusion • More complicated model • can change opinions… • Epidemics don’t always happen • Main parameters affecting: • no. of new infections caused by a “sick individual” • Starting points • single/multiple seed • individual’s “importance” in the network  

  17. An experiment • Ten Facebook friendship networks (4002500 nodes) • Run a SIS model (100 time-steps, average 10 runs) • Start from 1, 3 random nodes or “top” 3 nodes • “top” nodes identified via combined centrality metric • geometric mean of: degree, betwenness, closeness, PageRank, Katz index

  18. Modelling… At top infection spread: 3 Rnd vs. 1 Rnd +20% 3 Top vs. 3 Rnd +67% …marketers should pick highly connected persons as initial seeds if they hope to generate awareness or encourage transactions through their viral marketing campaigns since these hubs promise a wider spread of the viral message… (Hinz et al., 2011)

  19. Multi or Transdisciplinarity? • Contrary to widely held belief, the popular notion of a multidisciplinary approach is not a systems approach. [Gharajedaghi, 1999]

  20. …and Multimethod approach

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