QUANTITATIVE METHODS FOR STUDYING SOCIAL-ECOLOGICAL SYSTEMS

Transcription

1 QUANTITATIVE METHODS FOR STUDYING SOCIAL-ECOLOGICAL SYSTEMS PhD Programme in Sustainability Science at Stockholm Resilience Centre James Watson, Maja Schlüter, Steve Lade, Ingo Fetzer June 9-22, 2017 Where: Room: 239 When: Discussion: ; Lectures: ; Workshops Requirements: a laptop, an open mind, Software: R ( Website: TBD Course overview: 10 days over two weeks, each day will consist of one morning discussion focusing on the previous day s reading/homework, followed by a lecture on new material and finally a workshop in the afternoon. Lectures will focus on key social-ecological concepts and the language and approaches quantitative scientists use to describe and analyze them. Key point in the topics listed below, we aim to balance giving students new tools with which to perform quantitative analyses, and the concepts that are core to Social-ecological science. Our overarching aim is to make students aware that these quantitative approaches exist, and give them the language with which to talk to experts in these approaches. Core Teaching Concepts: 1) Philosophy of quantitative analysis and research design 2) Numbers, data, exploratory data analysis and statistics 3) Dynamical systems (equilibrium, stability, stocks and flows) 4) Complex Adaptive Systems (networks, emergence, self-organization, agentbased modeling) 5) Human Decision Making and Economics: optimization, game theory, adaptation and learning Learning outcomes: Understanding of how to conduct quantitative analysis of Social-Ecological Systems (SESs), and how to model (in the broadest sense). A vocabulary to talk with researchers doing ecological, economic, socialecological modeling of SES using statistical, mathematical or computational approaches. Overview of quantitative methods available for studying SES, particularly formal modeling, empirical analysis and methods from complexity science. Understanding of when and how different approaches can be used, their potentials and limitations (with based exposition of technical details). Understanding of different conceptualizations of SES, different approaches and their implications (e.g. what do we learn from a theoretical model, from a statistical analysis, etc.)

2 Reading material: THIS READING LIST WILL BE UPDATED CLOSER TO THE START OF THE COURSE Levin, S.A. et al Social ecological systems as complex adaptive systems: Modeling and policy implications. Environment and Development Economics, 18(2): Arrow KJ, Ehrlich P, Levin SA. Some Perspectives on Linked Ecosystems and Socioeconomic Systems. Available at SSRN: orhttp://dx.doi.org/ /ssrn Bousquet, F., Le Page, C., Multi-agent simulations and ecosystem management: a review. Ecological Modelling 176, doi: /j.ecolmodel Scheffer, M., & Carpenter, S. R. (2003). Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends in Ecology and Evolution. doi: /j.tree May, R. M., Levin, S. A., & Sugihara, G. (2008). Ecology for bankers. Nature, 451(February), Retrieved from papers3://publication/uuid/15ccda2f- 9D BA Page, S.E., What Sociologists Should Know About Complexity. Annual Review of Sociology 41, doi: /annurev-soc FB C7E

3 SCHEDULE This schedule will change the closer we get to the start date. Date Class Topic Week 1: Philosophy, Data, Dynamics Friday Jun 9, 2017 (James, Maja) Mon, June 12, 2017 (Steve) Tues June 13, 2017 (Steve) Afternoon lecture Philosophical Foundations Motivation for the course The philosophy of quantitative science. Intro to Complex Adaptive Systems Course purpose, review course material Social Ecological Systems are Complex Adaptive Systems: how do we analyze them? What is modeling? How do I design a quantitative research approach? Introduction to dynamical systems Fixed points, bifurcations, feedback loops Comparison to resilience concepts: regime shifts, resilience Graphical representations Plotting phase space and bifurcation diagrams Dynamical system models in practice Uses of dynamical system models Common building blocks Deductive and inductive approaches

4 Parametric and non-parametric approaches Numerics Develop dynamical model of course case study. Wed Jun 14, 2017 (Ingo) Thurs Jun 15, 2015 (Ingo) Introduction to data Introduction to DATA, the various forms it can take. Exploratory data analysis. Introduction to scientific computing with R Perform simple exploratory data analysis on course case study. (Own or examplary data) Philosophy of statistical modeling (linear-, non-linear models) Fri Jun 16, 2015 (Ingo + Steve) Develop statistical model of dynamical model case study (Do your own investigations with an exemplary Moth-dataset) Student group work / discussion on how what they have learned might impact their research Continue student group work / discussion.

5 Week 2: Complex Adaptive Systems, Strategy and Game theory, Networks, Agent-based modeling) Mon Jun 19, 2017 (Maja) SES as complex adaptive systems Introduction to CAS: complexity, adaptation and selection, emergence, self-organization, variation/heterogeneity. Intro to methods in complexity science, particularly ABM and networks Discuss the issue of scale and mean-field models; bottom-up versus systems-level modelling ( ) Intro to agent-based modeling using Netlogo. Develop Netlogo implementation Predator Prey system as ABM, comparison with dynamical system version. Tues Jun 20, 2017 Deleted: James Networks Introduction to connectivity, the different types. Examples from SES: food webs, social networks, metapopulations, cities etc. Introduction to network theory, Basic concepts from linear algebra (matrixes, vectors, elements) (James) Perform network analysis on data from ecological and the social sciences

6 Wed Jun 21, 2017 Human Decision Making Introduction to optimization (maximize, minimize) Basic Bioeconomics and Game theory Modeling adaptation and learning Thurs Jun 22, Game theory applied to the course case study Course Review Overview of course material Introduction to modules on advanced topics Student evaluations None