| Ambos lados da revisão anteriorRevisão anteriorPróxima revisão | Revisão anterior |
| geopro:pedro:abmdiscussions [2008/04/03 05:04] – pedro | geopro:pedro:abmdiscussions [2009/03/30 11:55] (atual) – pedro |
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| ====== Discussions about ABM ====== | ====== Discussions about ABM ====== |
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| | ====Modeling Complex Ecological Economic Systems==== |
| | |R Costanza, L Wainger, C Folke, K Mäler, 1993| BioScience, 43(8) 545-555| [[http://www.leg.ufpr.br/~pedro/papers/costanza_complex_systems.pdf|pdf]]| |
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| | {{http://www.leg.ufpr.br/~pedro/figures/costanza-triangle.jpg?270}} |
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| | ====Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science==== |
| | |R. Boero and F. Squazzoni, 2005| Journal of Artificial Societies and Social Simulation 8(4)| [[http://jasss.soc.surrey.ac.uk/8/4/6.html|html]]| |
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| | ** Abstract:** The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into **empirical data both to allow the calibration and the validation** of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies. |
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| | ====Key Challenges in Agent-Based Modelling for Geo-Spatial Simulation==== |
| | |A Crooks, C Castle, and M Batty, 2007| Agents2007| [[http://www.leg.ufpr.br/~pedro/papers/challenges-abm-geosimulation.pdf|pdf]]| |
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| | The five challenges that we see as important to their [agent-based models] development involve the following: |
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| | ^ purpose of the model | The purpose of agent-based models range from explanatory to predictive | |
| | ^ dependence of the model on theory | Agent-based models are being considered generic, independent of any particular field or application, and hence subject to use for any purpose that arise in an ad hoc way. In short, the scientific standards of the past are often buried in ad hoc model development | |
| | ^ representation of agents and their dynamics |Agents that do not move such as cells in cellular automata we would not define as agents in this context. **As we aggregate, it is more and more difficult to define relevant processes** | |
| | ^ calibration, validation and verification of the model against theory and data | |
| | ^ the development of operational models through software |the object oriented paradigm allows the integration of additional functionality from libraries not provided by the simulation/modelling toolkit. | Of particular interest here is the integration from GIS software libraries, which provide ABM toolkits with greater data management and spatial analytical capabilities. | |
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| | ====From Factors to Actors: Computational Sociology and Agent-Based Modeling==== |
| | |M. W. Macy and R. Willer , 2002| Annual Review of Sociology 28:143-166| |
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| | **Abstract:** Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on **the emergence of social structure and social order out of local interaction**. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform **virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility**. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach. |
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| | ====What is Ascape and Why Should You Care?==== |
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| | |M T Parker, 2001| Journal of Artificial Societies and Social Simulation|(4)1|[[http://www.soc.surrey.ac.uk/JASSS/4/1/5.html|html]]| |
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| | **Abstract:** Ascape is a framework designed to support the development, visualization, and exploration of agent based models. In this article I will argue that agent modeling tools and Ascape, in particular, can contribute significantly to the quality, creativity, and efficiency of social science simulation research efforts. Ascape is examined from the perspectives of use, design, and development. While Ascape has some unique design advantages, a close examination should also provide potential tool users with more insight into the kinds of services and features agent modeling toolkits provide in general. |
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| | There are very good tools (AgentSheets, StarLogo) available now that address this need for relatively |
| | simple models. For more complex (and in some cases, more testable) models, there is a significant gap. |
| | To address this gap, the development of significant end-user programming and composition features [...] |
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| ====Why are economists sceptical about agent-based simulations?==== | ====Why are economists sceptical about agent-based simulations?==== |
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| Technical: | Technical: |
| ^the treatment of time| discrete or continuous| | ^the treatment of time | discrete or continuous | |
| ^the treatment of fate| stochastic or deterministic| | ^the treatment of fate | stochastic or deterministic | |
| ^the representation of space| topology| | ^the representation of space | topology | |
| ^the population evolution |birth and death processes| | ^the population evolution |birth and death processes | |
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| Less technical: | Less technical: |
| ^the treatment of heterogeneity| which variables differ across individuals and how| | ^the treatment of heterogeneity | which variables differ across individuals and how | |
| ^ the interaction structure |localized or non-localized| | ^ the interaction structure |localized or non-localized | |
| ^the coordination structure |centralized, decentralized| | ^the coordination structure |centralized, decentralized | |
| ^ the type of individual behaviour| optimising, satisficing, etc.| | ^ the type of individual behaviour | optimising, satisficing, etc. | |
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| - **agents as a natural ontology for many social problem**: provides a place to express the enormous amount of data and knowledge about the behavior, motivations, and relationships of social agents, be they human individuals or institutions.// ABM must evolve not only in representation, but also in //case loading, uncertainty analysis, calibration of models to data, and methodologies for using models to answer specific questions or to solve problems. | - **agents as a natural ontology for many social problem**: provides a place to express the enormous amount of data and knowledge about the behavior, motivations, and relationships of social agents, be they human individuals or institutions.// ABM must evolve not only in representation, but also in //case loading, uncertainty analysis, calibration of models to data, and methodologies for using models to answer specific questions or to solve problems. |
| - **emergence**: as long as demonstrations of emergence are confined to the use of computer graphics for attractive demonstrations, the scientific importance of emergence and of ABM demonstration of emergent phenomena will remain small. Formal definition of what is meant by emergence is the exception rather than the rule, and quantitative tests that a given model achieves the sort of emergence advertised are rare. | - **emergence**: as long as demonstrations of emergence are confined to the use of computer graphics for attractive demonstrations, the scientific importance of emergence and of ABM demonstration of emergent phenomena will remain small. Formal definition of what is meant by emergence is the exception rather than the rule, and quantitative tests that a given model achieves the sort of emergence advertised are rare. |
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| Modelling with agents and space leads to four cases: | Modelling with agents and space leads to four cases: |
| * **Agents and environment both designed**. 'social laboratories', they serve as abstract thought experiments at best. | |
| * **Agents designed, environment analyzed**. robots designed to operate within pre-existing environments. can be effective in practice though they can be defeated by the complexity of the real environments within which they operate. | ^ ^ Agents designed ^ Agents analysed | |
| * **Agents analyzed, environment designed**. behavioral experiments where natural subjects are observed within controlled laboratory conditions. it is always questionable whether the rules thus derived will also be valid 'out there' in the real world. | ^ Environment designed |'social laboratories', they serve as abstract thought experiments at best. |behavioral experiments where natural subjects are observed within controlled laboratory conditions. it is always questionable whether the rules thus derived will also be valid 'out there' in the real world. | |
| * **Agents and environment both analyzed**. only that concerns LUCC. descriptive, predictive or explanatory models. a descriptive model can always be done given enough free parameters. Predictive models based on theory are by that token also explanatory models, though not all explanatory models are also predictive (e.g., the causal relations identified may change over time in unpredictable ways). Reasonably reliable predictive and explanatory models of land use change would be of tremendous value to planning and policymaking but after forty years of efforts in that area the success stories are still quite limited. | ^ Environment analysed |robots designed to operate within pre-existing environments. can be effective in practice though they can be defeated by the complexity of the real environments within which they operate. |only that concerns LUCC. descriptive, predictive or explanatory models. a descriptive model can always be done given enough free parameters. Predictive models based on theory are by that token also explanatory models, though not all explanatory models are also predictive (e.g., the causal relations identified may change over time in unpredictable ways). Reasonably reliable predictive and explanatory models of land use change would be of tremendous value to planning and policymaking but after forty years of efforts in that area the success stories are still quite limited. | |
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| The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. | The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. |
| | [[geopro:pedro:timed|Timed Automata]] | [[geopro:pedro:situated|Situated]] | | | [[geopro:pedro:timed|Timed Automata]] | [[geopro:pedro:situated|Situated]] | |
| | [[geopro:pedro:communication|Communication]] | [[geopro:pedro:boxed|Boxed Economy]] | | | [[geopro:pedro:communication|Communication]] | [[geopro:pedro:boxed|Boxed Economy]] | |
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| =====TODO===== | =====TODO===== |
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| | ====Critique on agent-based simulation==== |
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| | Cosma Shalizi (blog: three-toed sloth) has many good links where you might find something relevant. Here's some: |
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| | http://cscs.umich.edu/~crshalizi/notebooks/agent-based-modeling.html |
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| | http://www.cscs.umich.edu/~crshalizi/weblog/517.html |
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| | and his 'chaos, complexity and inference' course which puts ABM in a wider context: |
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| | http://cscs.umich.edu/~crshalizi/weblog/598.html |
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| | Somewhere - I can't find where - he also links to this paper: |
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| | http://www.isi.edu/~lerman/papers/isitr529.pdf |
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| | "A General Methodology for Mathematical Analysis of Multi-Agent Systems" which attempts to show that at least some set of ABMs can be replaced with differential equations - so an implicit critique. |
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| ====Multi-agent reinforcement learning for scheduling multiple-goals==== | ====Multi-agent reinforcement learning for scheduling multiple-goals==== |
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| ====Invariance and universality in social agent-based simulations==== | ====Invariance and universality in social agent-based simulations==== |
| |C Cioffi-Revilla, 2002|PNAS|[[http://leg.ufpr.br/~pedro/paper/pnas/invariance-universality-social-abm.pdf|pdf]]| | |C Cioffi-Revilla, 2002|PNAS|[[http://leg.ufpr.br/~pedro/papers/pnas/invariance-universality-social-abm.pdf|pdf]]| |
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| **Abstract:** Agent-based simulation models have a promising future in the social sciences, from political science to anthropology, economics, | **Abstract:** Agent-based simulation models have a promising future in the social sciences, from political science to anthropology, economics, |
