Generative social science Epstein, Joshua M; Epstein, Joshua M
2011., 20120102, 2012, 2007, 2007-01-01, 20060101, Letnik:
21
eBook
Agent-based computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet ...a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such far-flung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.
Agent zero Epstein, Joshua M; Epstein, Joshua M
2014., 2014, 2014-02-23, Letnik:
25
eBook
The Final Volume of the Groundbreaking Trilogy on Agent-Based Modeling In this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero. This software individual, or ..."agent, " is endowed with distinct emotional/affective, cognitive/deliberative, and social modules. Grounded in contemporary neuroscience, these internal components interact to generate observed, often far-from-rational, individual behavior. When multiple agents of this new type move and interact spatially, they collectively generate an astonishing range of dynamics spanning the fields of social conflict, psychology, public health, law, network science, and economics.Epstein weaves a computational tapestry with threads from Plato, Hume, Darwin, Pavlov, Smith, Tolstoy, Marx, James, and Dostoevsky, among others. This transformative synthesis of social philosophy, cognitive neuroscience, and agent-based modeling will fascinate scholars and students of every stripe. Epstein's computer programs are provided in the book or on its Princeton University Press website, along with movies of his "computational parables.? Agent_Zero is a signal departure in what it includes (e.g., a new synthesis of neurally grounded internal modules), what it eschews (e.g., standard behavioral imitation), the phenomena it generates (from genocide to financial panic), and the modeling arsenal it offers the scientific community. For generative social science, Agent_Zero presents a groundbreaking vision and the tools to realize it.
Agent-based computational models can capture irrational behaviour, complex social networks and global scale--all essential in confronting H1N1, says Joshua M. Epstein.
Triple contagion: a two-fears epidemic model Epstein, Joshua M; Hatna, Erez; Crodelle, Jennifer
Journal of the Royal Society interface,
08/2021, Letnik:
18, Številka:
181
Journal Article
Recenzirano
Odprti dostop
We present a differential equations model in which contagious disease transmission is affected by contagious fear of the disease and contagious fear of the control, in this case vaccine. The three ...contagions are coupled. The two fears evolve and interact in ways that shape distancing behaviour, vaccine uptake, and their relaxation. These behavioural dynamics in turn can amplify or suppress disease transmission, which feeds back to affect behaviour. The model reveals several coupled contagion mechanisms for multiple epidemic waves. Methodologically, the paper advances infectious disease modelling by including human behavioural adaptation, drawing on the neuroscience of fear learning, extinction and transmission.
Why Model? Epstein, Joshua M
Journal of artificial societies and social simulation,
10/2008, Letnik:
11, Številka:
4
Journal Article
Recenzirano
This lecture treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, ...and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and 'summarize' data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author's 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute. Adapted from the source document.
The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents-fully endowed with rules and parameters-to grow macroscopic target ...patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target-the
problem-we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators.
-
. This is the backward problem and tools from Evolutionary Computing can help us solve it. In this overarching essay of the current JASSS Special Section, Part 1 discusses the motivation for iGSS. Part 2 discusses its
, as distinct from other approaches. Part 3 discusses
, previewing the five iGSS applications that follow. Part 4 discusses several
for agent-based modeling and economics. Part 5 proposes
: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 6. Looking 'backward to the future,' I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.
The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of ...several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM's speed and scalability.
In classical mathematical epidemiology, individuals do not adapt their contact behavior during epidemics. They do not endogenously engage, for example, in social distancing based on fear. Yet, ...adaptive behavior is well-documented in true epidemics. We explore the effect of including such behavior in models of epidemic dynamics.
Using both nonlinear dynamical systems and agent-based computation, we model two interacting contagion processes: one of disease and one of fear of the disease. Individuals can "contract" fear through contact with individuals who are infected with the disease (the sick), infected with fear only (the scared), and infected with both fear and disease (the sick and scared). Scared individuals--whether sick or not--may remove themselves from circulation with some probability, which affects the contact dynamic, and thus the disease epidemic proper. If we allow individuals to recover from fear and return to circulation, the coupled dynamics become quite rich, and can include multiple waves of infection. We also study flight as a behavioral response.
In a spatially extended setting, even relatively small levels of fear-inspired flight can have a dramatic impact on spatio-temporal epidemic dynamics. Self-isolation and spatial flight are only two of many possible actions that fear-infected individuals may take. Our main point is that behavioral adaptation of some sort must be considered.
Planning for a possible influenza pandemic is an extremely high priority, as social and economic effects of an unmitigated pandemic would be devastating. Mathematical models can be used to explore ...different scenarios and provide insight into potential costs, benefits, and effectiveness of prevention and control strategies under consideration.
A stochastic, equation-based epidemic model is used to study global transmission of pandemic flu, including the effects of travel restrictions and vaccination. Economic costs of intervention are also considered. The distribution of First Passage Times (FPT) to the United States and the numbers of infected persons in metropolitan areas worldwide are studied assuming various times and locations of the initial outbreak. International air travel restrictions alone provide a small delay in FPT to the U.S. When other containment measures are applied at the source in conjunction with travel restrictions, delays could be much longer. If in addition, control measures are instituted worldwide, there is a significant reduction in cases worldwide and specifically in the U.S. However, if travel restrictions are not combined with other measures, local epidemic severity may increase, because restriction-induced delays can push local outbreaks into high epidemic season. The per annum cost to the U.S. economy of international and major domestic air passenger travel restrictions is minimal: on the order of 0.8% of Gross National Product.
International air travel restrictions may provide a small but important delay in the spread of a pandemic, especially if other disease control measures are implemented during the afforded time. However, if other measures are not instituted, delays may worsen regional epidemics by pushing the outbreak into high epidemic season. This important interaction between policy and seasonality is only evident with a global-scale model. Since the benefit of travel restrictions can be substantial while their costs are minimal, dismissal of travel restrictions as an aid in dealing with a global pandemic seems premature.
Depressional wetlands influence the functions of wetlandscapes by storing and releasing water, providing critical habitat, amplifying carbon and nutrient cycling, and influencing microclimate. ...Despite persistent subsurface connectivity, depressional wetlands are surrounded by uplands so only sporadically connect via surface pathways. However, the frequency, duration, and relative importance of surface connectivity in depressional wetlands remains poorly understood, limiting quantification of their landscape functions. Using multiple years of stage variation in 67 depressional wetlands across four contrasting wetlandscapes, we observed wetland spill elevation is exceeded 10%–40% of the time, with substantial variation within and between wetlandscapes. Moreover, surface connectivity increased water loss rates by 200%–350% on a depth basis and 350%–850% on a volumetric basis compared with subsurface water loss rates. This temporally disproportionate water export suggests that short‐lived surface connectivity is crucial for aggregate landscape export of water‐borne materials and numerous hydrologic and habitat services. Contrasting water loss rates above and below spill thresholds create homeostatic feedback that stabilizes water levels near the thresholds. We explored geomorphic, climatic, and vegetative influences on hydrologic connectivity, quantified as nighttime recession rates below the spill threshold for groundwater connectivity and percent time above thresholds for surface connectivity. Groundwater connectivity was consistently greater in deeper wetlands and wetlandscape identity was the primary factor explaining variation in surface and subsurface connectivity. Our results highlight the critical role of surface connectivity in coastal plain wetlands, illustrate the heterogeneity of those wetland functions within and across wetlandscapes, and provide hydrologic benchmarks for evaluating restoration of aggregate landscape functions.
Key Points
Wetlands in coastal plain wetlandscapes are episodically surface connected, during which hydrological exchanges increase five fold
Wetland basin depth appears to control the baseline groundwater recession rate
In humid settings, slow rates of subsurface hydrological export result in wetland water levels poised at surface connectivity
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