Kristian Lindgren is Professor in Complex Systems at Chalmers University of Technology in Gothenburg, Sweden. He started the International Master Program in Complex Adaptive Systems, in year 2000, and he is Director of the Graduate School (Ph.D.) for Complex Systems at Chalmers. He has a background in engineering physics, and he has been working with complex systems in various disciplines since the 1980's. He earned his Ph.D. in physical resource theory from Chalmers in 1988. His research areas include information theory for complex and self-organizing systems, game theory of evolutionary systems, and agent-based modeling of societal systems. Since the mid 1990's He has also been working in the area of energy systems with development of models of regional and global energy systems in a climate change perspective. He is currently leading a research project on an agent-based approach for analysis and modelling of energy systems in transition.
Agent-Based Approaches for Analysis and Modelling of Land-Use and Energy Systems
A future energy system that to a large extent depends on variable renewable energy sources, like wind and solar power, involves new challenges for securing a reliable supply of electricity. The economic basis for such an electricity system differs significantly from the one we have today, and one can expect that prices will be more volatile and risk for shortages in supply increases. In the transition towards a renewable energy system, foresight is needed in order to design policies that facilitate changes that are required for the transition. This also includes improved understanding of possible consequences of implemented measures and policies-also the unintended ones, like the increased price volatility in the example above. Another example of undesired consequences that has been discussed as a possible effect of climate policies is the food price peaks we have witnessed the passed ten years. The argument has been that the rapidly increased demand for bioenergy from agricultural land has led to increased prices on several agricultural commodities, further amplified by subsequent political decisions like export restrictions in certain countries. In order to address these issues, we have developed a new set of models that explicitly includes mechanisms for decisions-mechanisms that do not necessarily assume rational agents but may allow for bounded rationality. The investments that such an agent-based model results in typically differs from what an optimisation model would suggest. Since optimisation modelling is the dominating tool for assessments of energy systems futures, our major aim is to critically and carefully investigate differences between these modelling approaches. I will present a complex systems perspective on agent-based modelling, aiming at applications in the area of land-use, energy systems and climate change. I will exemplify how the agent-based modelling differs from the optimisation/equilibrium approach by describing two conceptual models in the areas of agricultural land-use and power systems development, respectively. In the case of land-use, the focus is on the decisions by land-users and what crop to sow, while in the power system case we model the decision on what plant to invest in. In both these cases we have also an optimisation/equilibrium model to compare with that has the same micro-economic characteristics. I will also illustrate that the agent-based model in the land use case, under certain circumstances, can be projected to an aggregate dynamics, where variables are expressed in terms of prices and quantities. For this to be possible, some of the more interesting agent characteristics need to be removed. The advantage is instead that we get a dynamical systems model of lower dimension that can more easily been analysed in order to determine stability characteristics. This means that we have three different models for the same system: an agent-based model, a dynamic model of aggregate quantities, and an optimisation/equilibrium model. Both for the land-use system and for the power system case, the focus is on what new dynamic and stability characteristics of the system that can be captured by the use of an agent-based or dynamic modelling approach.
Andrea Roventini is associate professor at the Institute of Economics, Scuola Superiore Sant'Anna, Pisa (Italy). His main research interests include complex system analysis, agent-based computational economics, business cycles, and the study of the effects of monetary, fiscal, technology and climate policies. He is involved in the following projects financed by the European Commission: “IMPRESSIONS: impacts and risks from higher-end scenarios: strategies for innovative solutions”; “DOLFINS: Distributed Global Financial Systems for Society”; “ISIGrowth: Innovation-fuelled, Sustainable, Inclusive Growth. His works have been published in Journal of Evolutionary Economics, Journal of Applied Econometrics, Journal of Economic Dynamics and Control, Environmental Modelling and Software, Macroeconomic Dynamics. He is advisory editor of the Journal of Evolutionary Economics.
Faraway, So Close: An Agent-Based Model for Climate, Energy and Macroeconomic Policy
In the paper we develop the first agent-based integrated assessment model, which offers an alternative to standard, computable general-equilibrium frameworks. The Dystopian Schumpeter meeting Keynes (DSK) model is composed of heterogeneous firms belonging to capital-good, consumption-good and energy sectors. Firms' production leads to greenhouse gas emissions, which affect temperature dynamics in a non-linear way. Increasing temperature triggers climate damages hitting, at the micro-level, workers' labor productivity, energy efficiency, capital stock and inventories of firms. In that, aggregate damages are emerging properties of the out-of-equilibrium interactions among heterogeneous and boundedly rational agents. We find the DSK model is able to account for a wide ensemble of micro and macro empirical regularities concerning economic and climate dynamics. The model is employed to test the effects of a variety of micro-level climate shocks on the economy's performances in terms of growth, unemployment and likelihood of crises. We find that different types of shocks have largely heterogeneous impacts, with labour productivity and capital stock shocks producing the most harmful impacts. We show that the magnitude and the uncertainty associated with climate change impacts increase over time, and that climate damages much larger than those estimated through standard IAMs. Remarkably, we find that under specific circumstances the economy ends up to be trapped in a low growth high volatility state. Our results suggest the presence of tipping points and irreversible trajectories, thereby suggesting the need of urgent policy interventions. Moreover, they highlight the role of agents' heterogeneity and interactions, evolutionary technical change and demand-driven firm investment dynamics in the transmission and amplification of climate shocks. As a future development, the model will be used as a policy laboratory to test different ensembles of interventions. In particular, we will account for the combination of climate (mitigation and adaptation), fiscal, innovation and monetary policies. The identification of those interventions that are compatible with the 1.5 degree target will be the major objective of our future investigation.
Jürgen Scheffran is Professor in the Institute of Geography at Universität Hamburg und head of the Research Group Climate Change and Security (CLISEC) which is part of the Excellence Cluster Integrated Climate System Analysis and Prediction (CliSAP) and the Center for Earth System Research and Sustainability (CEN). After his PhD in physics at Marburg University he worked in the IANUS research group at the Technical University Darmstadt, the Potsdam Institute for Climate Impact Research, as Visiting Professor at the University of Paris (Sorbonne), and at the University of Illinois in the Departments of Political Science and Atmospheric Science as well as interdisciplinary research groups (ACDIS, CABER, EBI). His fields of research and project activities include: climate change and security; land use and energy-water-food nexus; resource conflicts and human migration; complex systems, agent-based modelling and social network analysis in human-environment interaction; sustainability science, technology assessment and international security. He was consultant to the United Nations, the Technology Assessment Bureau of the German Parliament, the Federal Environmental Agency, and the German delegation to the climate negotiations.
Agents, Coalitions and Social Networks in Environmental Conflict and Cooperation
To study the dynamics of conflict and the evolution of cooperation in human-environment interaction, an integrated agent-based modeling framework is presented. The VIABLE model (Values and Investments in Agent-Based interaction and Learning for Environmental systems) describes the dynamic behavior, interactions and games of agents who pursue objectives by allocating their investments to different action pathways that change the environment. Over time, agent actions are adjusted through repeated feedback and learning cycles according to decision and adaptation rules in response to environmental changes, the actions of other agents, and the assessment of expected benefits, costs and risks associated with the action paths.
Agents here stand for multinational organizations, nations, states, communities, companies, non-governmental organisations and citizens that collaborate or compete on local, regional, national, and global levels by taking joint actions and setting targets. Within this framework, it is possible to analyse the complexity and stability of pathways, social dilemmas and complex crises, as well as adaptive transition and transformation processes, across micro, meso and macro scales. Emerging collective behaviour and social interaction patterns include switching from conflict to cooperation, the evolution of social networks, the diffusion of innovations, market trading and pricing, the establishment of norms, institution building and the formation or breakup of coalitions among multiple agents.
The framework is applied and compared for specific cases of human-environment interactions, including fishery management, sustainable water and land use, resource conflicts, low-carbon energy transitions, climate change and emission trading systems. A particular focus will be on mechanisms to reduce emissions by establishing limits on total emissions, distribution of tradable permits and carbon market prices, which are relevant for the COP Paris Agreement of the UN Framework Convention on Climate Change (UNFCCC).
Klaus G. Troitzsch
Klaus G. Troitzsch was full Professor for computer applications in the social sciences at the University of Koblenz-Landau between 1986 and 2012. He studied sociology and political science in Cologne and Hamburg. He contributed to the methodological development of computer-based simulation, especially agent-based modelling in the social sciences. Among his research projects are GLODERS (Global Dynamics of Extortion Racket Systems), OPOCOMO (Open COllaboration of POlicy MOdeling), EMIL (Emergence in the Loop: Simulation the two-way dynamics of norm innovation) and FIRMA (Freshwater Integrated Resources Management with Agents).
CO2 Reduction Strategies in a Simplified Multipolar Artificial World. The Role of "Alternative Facts" in CO2 Policy Making
The talk will be about a simplified artificial world consisting of several countries whose leaders apply differently parameterised strategies with respect to their CO2 emission, diffusion and absorption. The research question which the talk wants to answer is about the consequences of different perceptions about the CO2 cycle: The leaders of the different countries have incomplete knowledge of the mechanisms of CO2 emission, diffusion and absorption both by land plants and by the ocean.
What they can observe is the current ocean acidification along their coastlines, the current overall CO2 concentration of the whole virtual world as well as a short history of the CO2 concentration in their own countries. They have different beliefs above which threshold the acidification and the overall CO2 concentration might become dangerous, and from the short time series of the local CO2 concentration they calculate whether this concentration has decreased or increased or remained more or less stable. These observations control the strategies the country leaders apply to CO2 emission control: Whenever they have reason to believe one of thresholds will soon be reached or when they found out that the CO2 concentration has increased in the recent past, they limit the CO2 emission ceilings in their countries and continue their observations. Whenever they see that their own CO2 emission reduction is not sufficient to avoid additional acidification or increase of the total concentration the country with the lowest emission asks the country with the highest current emission to reduce its high CO2 emission, too, and latter has a certain propensity to follow such a request.
The parameters of the model are the absorption capacities of land and ocean (representing the "laws of nature" in this simplified virtual world), the initial CO2 emission thresholds imposed on CO2 sources in the different countries by the country leaders, the propensities to abide by emission reduction requests and the combination of strategies applied by the country leaders (all of the latter representing the action rules of the country leaders).
The model tries to find out whether there are parameter combinations that allow for sufficient CO2 reduction in this simplified multipolar artificial world.