SEGS Research: Simulations

A computer simulation is a computational model that reproduces the behavior of a system. We use computer simulations to reveal emergent properties of complex social-ecological systems, such as biosecurity in livestock industry networks, transportation governance networks, and water quality in a lake embedded in an agricultural landscape with multiple levels of jurisdictional governance.

  • Simulating Heterogeneous Farmer Behaviors under Different Policy Schemes: Integrating Economic Experiments and Agent-Based Modeling

    Shang Wu, Asim Zia, Mengyuan Ren, Kent Messer

    2017. Policy and Complex Systems 3(2) 164-188

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    In this paper, we develop an agent-based model that scales up results from economic experiments on technology diffusion and abatement of non-point source water pollution under the conditions of an actual watershed. The results from the economic experiments provide the foundation for assumptions used in the agent-based model. Data from geographic information systems and the U.S. census of agriculture initialize and parameterize the model. This integrated model enables the exploration of the effects of several policy interventions on technology diffusion and agricultural production and, hence, on agricultural non-point source pollution. Simulation results demonstrate that information "nudges" based on social comparisons increase ambient-based policy performance as well as efficiency, especially individual-level tailored information on what others like them have done in similar situations.
  • Agent Based Modeling of Intergovernmental Networks: Harnessing Experimental Simulations for Transportation Policy Informatics.

    Zia, A., Koliba, C.

    2017. Paper presented at the 35th International Conference of the System Dynamics Society

    Agent-based models can be deployed as policy informatics platforms to track resource flows and distributions under differential configurations of inter-governmental networks. This study provides a detailed application of a policy informatics platform in the contested arena of transportation policy implementation networks across federal, the state of Vermont and its regional and local governments. Through this policy informatics platform, three specific questions are addressed: (1) How much weight can be accorded to state versus regional versus local government priorities in funding transportation infrastructure development projects? (2) What are the trade-offs between efficient maintenance of the transportation system versus equitable access to the transportation services? (3) What are the sources of uncertainty in prioritizing transportation infrastructure projects and how is this uncertainty quantified? A Pattern-Oriented, Agent Based Model (ABM) of a transportation governance network, calibrated for the state of Vermont including its regional and local town governments, is presented. This ABM simulates the dynamics of transportation project prioritization processes under alternate intergovernmental institutional rule structures. This study demonstrates a practical and detailed application of a policy informatics platform by showing how experimental simulations may be used to evaluate the design of inter-governmental policy implementation networks and their impacts on policy outcomes.
  • Intergovernmental Dynamics of Transportation Planning: Why Some Towns Attract More Federal Funding Than Others?

    Zia, A., Koliba, C.

    2017. Paper presented at the 35th International Conference of the System Dynamics Society

    This paper explores the dynamics of intergovernmental relations in the distribution of transportation funds from the viewpoint of local governments. We address the following questions: (1) why some towns attract more federal and state funding than other towns? (2) Are there some balancing and reinforcing feedback loops that influence some towns attracting more funds than others? We addressed these research questions through developing a stakeholder informed system dynamic model with an explicit focus on the intergovernmental influence (exogenous) and local town level technical and financial capacity (endogenous) dynamics. The model is calibrated to two local towns in Vermont. The model simulates two balancing loops (BL) and three reinforcing loops (RL). BL1: As a jurisdiction receives more transportation funds, they are able to meet more of their transportation needs and require fewer funds in the short term. BL2: With more development, there is less capacity to continue to build, so less money is allocated for new development. Three RLs include more money received leading towards more experience and thus greater technical capacity, more technical capacity directing a jurisdiction to more support from the MPO, and more transportation needs requires more transportation funds which ultimately gives a jurisdiction more financial capacity.
  • More Complex Complexity: Exploring the Nature of Computational Irreducibility Across Physical, Biological, and Human Social Systems.

    Beckage, B., Kauffman, S. Zia, A., Koliba, C., Gross, L.J.

    2013. Zenil, H. editor. Irreducibility and Computational Equivalence. Berlin: Springer-Verlag. 79-88.

    The predictability of many complex systems is limited by computational irre- ducibility, but we argue that the nature of computational irreducibility varies across physical, biological and human social systems. We suggest that the com- putational irreducibility of biological and social systems is distinguished from physical systems by functional contingency, biological evolution, and individual variation. In physical systems, computationally irreducibility is driven by the interactions, sometimes nonlinear, of many different system components (e.g., particles, atoms, planets). Biological systems can also be computationally irre- ducible because of nonlinear interactions of large number of system components (e.g., gene networks, cells, individuals). Biological systems additionally create the probability space into which the system moves: Biological evolution creates new biological attributes, stores this accumulated information in their genetic code, allows for individual genetic and phenotypic variation among interacting agents, and selects for the functionality of these biological attributes in a con- textual dependent manner. Human social systems are biological systems that include these same processes, but whose computational irreducibility arises from sentience, i.e., the conscious perception of the adjacent possible, that drives social evolution of culture, governance, and technology. Human social systems create their own adjacent possible through the creativity of sentience, and accu- mulate and store this information culturally, as reflected in the emergence and evolution of, for example, technology. The changing nature of computational irreducibility results in a loss of predictability as one moves from physical to biological to human social systems, but also creates a rich and enchanting range of dynamics.
  • An Interactive Land Use Transition Agent-Based Model (ILUTABM): Endogenizing Human Environment Interactions at Watershed Scales.

    Tsai, Y., Zia, A., Koliba, C., Bucini, G., Guilbert, J., and Beckage,

    2015. Land Use Policy.

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    Forest Transition Theory (FTT) suggests that reforestation may follow deforestation as a result of and interplay between changing social, economic and ecological conditions. We develop a simplistic but empirically data driven land use transition agent-based modeling platform, interactive land use transition agent-based model (ILUTABM), that is able to reproduce the observed land use patterns and link the forest transition to parcel-level heuristic-based land use decisions and ecosystem service (ES). We find that, when farmers value food provisioning Ecosystem Services (ES) more than other ES (e.g., soil and water regulation), deforestation is observed. However, when farmers value less food provisioning than other ES or they value food provisioning and other ES equally, the forest transition is observed. The ILUTABM advances the Forest Transition Theory (FTT) framework by endogenizing the interactions of socio-ecological feedbacks and socio-economic factors in a generalizable model that can be calibrated with empirical data.
  • Using System Dynamics Modeling to Project Critical Care Pathways Populations and Costs for End Stage Renal Disease (ESRD): US Population to 2020.

    Fernandez, L., Koliba, C., Cheung, K., Zia, A., Jones, C., and Solomon, R.

    2015. Journal of Health Economics and Outcomes Research. 3(1): 24-33.

    End Stage Renal Disease (ESRD) accounts for 9% of Medicare spending, with the beneficiaries suffering from ESRD costing 7-9 times more than the average. This population is expected to continue to grow as a portion of Medicare beneficiaries. To provide clinicians and administrators with a greater understanding of the combined costs associated with the multiple critical care pathways for End Stage Renal Disease we have developed a model to predict ESRD populations through 2020. A system dynamics model was designed to project the prevalence and total costs of ESRD treatment for the United States through 2020. Incidence, transplant and mortality rates were modeled for 35 age and primary diagnosis subgroups coursing through different ESRD critical care pathways. Using a web interface that allows users to alter certain combinations of parameters, several demonstration analysis were run to predict the impact of three policy interventions on the future of ESRD care The model was successfully calibrated against the output of United States Renal Data System’s (USRDS) prior predictions and tested by comparing the output to historical data. Our model predicts that the ESRD patient population will continue to rise, with total prevalence increasing to 829,000 by 2020. This would be a 30% increase from the reported 2010 prevalence. Findings suggest that clinical care and policy changes can be leveraged to more effectively and efficiently manage the inevitable growth of ESRD patient populations. Patients can be shifted to more effective treatments, while planning integrating systems thinking can save Medicare’s ESRD program billions over the next decade.
  • The Emergence of Attractors Under Multi-Level Institutional Designs: Agent-based Modeling of Intergovernmental Decision Making for Funding Transportation Projects.

    Zia, A. and Koliba, C.

    2013. Artificial Intelligence (AI) & Society.

    Multi-level institutional designs with distributed power and authority arrangements among federal, state, regional, and local government agencies could lead to the emergence of differential patterns of socioeconomic and infrastructure development pathways in complex social–ecological systems. Both exogenous drivers and endogenous processes in social–ecological systems can lead to changes in the number of ‘‘basins of attraction,’’ changes in the positions of the basins within the state space, and changes in the positions of the thresholds between basins. In an effort to advance the theory and practice of the governance of policy systems, this study addresses a narrower empirical question: how do inter- governmental institutional rules set by federal, state, and regional government agencies generate and sustain basins of attraction in funding infrastructure projects? A pattern- oriented, agent-based model (ABM) of an intergovernmental network has been developed to simulate real-world transportation policy implementation processes across the federal, the state of Vermont, regional, and local governments for prioritizing transportation projects. The ABM simulates baseline and alternative intergovernmental institutional rule structures and assesses their impacts on financial investment flows. The ABM was calibrated with data from multiple focus groups, individual interviews, and analysis of federal, state, and regional scale transportation projects and programs. The results from experimental simulations are presented to test system-wide effects of alternative multi-level institutional designs, in particular different power and authority arrangements between state and regional governments, on the emergence of roadway project prioritization patterns and funding allocations across regions and towns.