Social Simulation and Visual Insights
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The document explores the concept of simulation as a metaphorical model that simplifies complex systems, providing insights into social behaviors such as dating and education's impact on inflation. It emphasizes the utility of computer simulations and agent-based modeling in facilitating theoretical understanding and experimentation in social sciences. Key insights include the importance of intuition, the potential of simulations to challenge conventional wisdom, and the significance of simplicity in modeling assumptions to produce fruitful outcomes.
Social Simulation and Visual Insights
- 1. Social simulation visual insights
- 4. What is “simulation”? “ Simulations are metaphorical models that symbolically ‘stand for’ something else” Pattee, HH: 1996, Simulations, realizations, and theories of life, in Boden, M. (ed), The Philosophy of Artificial Life , Oxford University Press, New York, pp. 379 –393
- 5. What is “simulation”? “ A model is a simplification (smaller, less detailed, less complex, or all of these together) of some other structure or system” Gilbert, N and Troitszch, K: 1999, Simulation for the Social Scientist, Open University Press, Philadelphia
- 8. What is “simulation”? The mate searching game: When should you stop looking and dating?
- 9. The mate searching game
- 10. What is “simulation”? “ you might want to settle down with a partner but the partner may still be wanting to continue to search for someone else” Gilbert, N and Troitszch, K: 1999, Simulation for the Social Scientist, Open University Press, Philadelphia
- 11. What is “simulation”? “ We can observe dating behaviour going on all the time but the underlying strategies that people use are hard to discover directly, so simulation can be useful.” Gilbert, N and Troitszch, K: 1999, Simulation for the Social Scientist, Open University Press, Philadelphia
- 12. http :// www.bized.co.uk /virtual/ economy / model / Easy.html Increase in inflation if education expenditure is increased by 10%
- 13. Simulation + visual insights “ The high quality of computer simulations and graphic displays can provide a new form of artificial empiricism to test theories more efficiently” Pattee, HH: 1996, Simulations, realizations, and theories of life, in Boden, M. (ed), The Philosophy of Artificial Life , Oxford University Press, New York, pp. 379 –393
- 14. Simulation + theory “ Mathematical models are often analytically intractable . When this is so, computers can be used to get a feel for the model’s dynamics. Computers are used here as aids to the theorist. They help you think ” Sober, E.: 1996, Learning from functionalism –prospects for strong Artificial Life, in Boden, M. (ed), The Philosophy of Artificial Life, Oxford University Press, New York, pp. 361 –378
- 16. Simulation + understanding “ Often the most efficient way to predict and understand the behavior of a novel complex system is to construct the system and observe it” Simon, HA: 1995, Artificial Intelligence: an Empirical Science, Artificial Intelligence, 77, 95-127
- 17. Simulation + complexity Simple systems… can be modelled mathematically, but… “ when matters get a little more complex… the system is studied by carrying out simulations for various values of the parameters” Simon, HA: 1995, Artificial Intelligence: an Empirical Science, Artificial Intelligence, 77, 95-127
- 18. http :// simcitysocieties.ea.com / index.php
- 19. Simulation + science Simulation… “is a third way of doing science. Whereas the purpose of induction is to find patterns in data and that of deduction is to find consequences of assumptions, the purpose of agent-based modelling is to aid intuition ” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 20. Simulation + experimentation “ Agent-based modelling is a way of doing thought experiments ” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 21. Simulation + mathematics “ Simulation is necessary because the interactions of adaptive agents typically lead to nonlinear effects that are not amenable to the deductive tools of formal mathematics” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 22. Simulation + complexity “ The complexity of agent-based modelling should be in the simulated results , not in the assumptions of the model” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 24. Simulation + complexity “ Simplicity of the assumptions is important, and realistic representation of all details is not. Exploratory models should be judged by their fruitfulness , not by their accuracy” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 25. Simulation + prediction “ The main purpose of agent-based models is not prediction but a deeper understanding of how fundamental social processes operate” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 26. Simulation + interpretation “ Perhaps the most useful outcome of a simulation model is to provide new ways of thinking about old problems” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 27. Simulation + intuition “ Perhaps the most important lesson is that intuition is not a very good guide to predicting what even a very simple dynamic model will produce” Axelrod, R.: 1997, Advancing the art of simulation in the social sciences, in Conte, R., Hegselmann, R. and Terna, P. (eds), Simulating Social Phenomena, pp. 21-40, Springer, Berlin
- 29. Simulation + complexity The most surprising lesson we have learned from simulating complex physical systems on computers is that: complex behaviour need not have complex roots Langton, 1989
- 31. Culture and Complexity: New Insights on Organisational Change Richard Seel
- 33. Claims “ A simulation of life can be very instructive both empirically and theoretically, but we must be explicit about what claims we make for it” Pattee, HH: 1996, Simulations, realizations, and theories of life, in Boden, M. (ed), The Philosophy of Artificial Life , Oxford University Press, New York, pp. 379 –393
- 34. Para leer El Arbol Del Conocimiento: Las Bases Biológicas Del Entendimiento Humano Maturana R., Humberto, Varela, Francisco (Lumen) ISBN: 9870003583. ISBN-13: 9789870003588