Modeling Complex Systems for Public Policies International Seminar Brasília, September 1-4 Department for Regional, Urban and Environmental Studies and Policy Institute for Applied Economic Research
Thanks! 2
Dirur Ipea UCP s team IADB Loan Contract Number 1841/OC-BR CNPq Thanks 3
Bernardo Alves Furtado Patrícia Alessandra Morita Sakowski Coordinators 4
Marina Haddad Tóvolli Research assistant 5
Rogério Boueri Miranda Cleandro Henrique Krause Júlio César Roma Acir dos Santos Almeida Leonardo Monteiro Monasterio Nilo Luiz Saccaro Júnior Team @Ipea 6
William Rand, University of Maryland Miguel Fuentes, Santa Fe Institute Jaime Simão Sichman, University of São Paulo Claudio J. Tessone, ETH Zürich Herbert Dawid, Bielefeld University Luis Bettencourt, Santa Fe Institute Pablo Marquet, Santa Fe Institute Dick Ettema, Utrecht University Michael J. Jacobson, University of Sydney Bernardo Mueller, University of Brasília Yaneer Bar-Yam, NECSI Privilege. Consultants 7
James E. Gentile, The MITRE Corporation Orlando Manuel da Costa Gomes, Lisbon Polytechnic Institute & Lisbon University Institute Marcos Aurélio Santos da Silva, Embrapa Tabuleiros Costeiros Contributions 8
Lucio Rennó, University of Brasília Daniel Cajueiro, University of Brasília Sergei Soares, President Ipea Special thanks for discussants 9
Project Objective Book Concepts Methods Operationalizing Public Policies phenomena as Complex Objects Applications: Transport, Legislative Process, Education, Brazil, world Why CS for Public Policies Outline 10
Modeling... Complex Systems... for Public Policies! 11
Research design: applications for public policies Seminal Book: Public Policies, Applications, Brazil Seminar: exchange & cohesion Modeling: spatial fiscal analysis and education Project 12
I. Effectiveness of Public Policies in Brazil by promoting methodologies II. Diffusion and exchange of frontier knowledge III. Advances in applied modeling for the case of Brazil Objectives 13
Listen. Make questions. Learn from experience. Seminar: debate 14
Book 15
interaction among agents and the environment; in a complex, non-linear and dynamic manner following simple rules; actions generate emergent behavior, different behavior at different scales; consider feedbacks, systems that adapt, learn, evolve, Concepts: CS s approach 16
Simulate actions and interactions among citizens, firms, institutions Constrained by legislation, budget, political and spatial boundaries Computational environment with essence of the system CS to Public Policies 17
Networks, information theory Agent-based modeling (ABM), cellular automata (CA) Data science, data mining, machine learning Chaos theory, system dynamics Pattern formation, Game theory, Methods: briefly 18
Python, SimPy, NetworkX Matlab, Mathematica R, QGIS NetLogo Ucinet, MASON, Swarm, RePast, Flame, MASS +78 Tools 19
Public Policies as complex objects 20
Social systems Economic systems Urban systems Environmental systems Public Policies objects as CS 21
Applications: Brazil 22
Urban and Transportation Urban Economics Land Use/transport Literature/Methodologies CA Network Analysis ABM Machine Learning Economics Economic Growth ABM Financial Market Complexity Super- Network Network Measures statistics Analysis Analysis Abdoos et al. (2011) Almeida-Filho (2006)* Alston et al. (2014)* Alvarenga (2008)* Amarante and Bazzan (2012) Andrade and Frazzon (2012)* Avancini (2013)* Barbosa et al. (2013)* Bastos (2011) Bazzan et al. (2010) Bazzan et al. (2011) Berger and Borenstein (2013)* Cajueiro and Tabak (2004)* Cajueiro and Tabak (2008)* Carvalho (2012)* Costa et al. (2012) Delaneze et al. (2011)* Feitosa et al. (2012)* Furlan (2012) Furtado (2009)* Furtado and van Delden (2011)* Gagliardi and Alves (2005) Gleria et al. (2002)* Guimarães et al. (2009) Hausmann et al. (2014)* Jacintho et al. (2010)* Lim et al. (2002)* Marteleto (2001) Matsushita et al. (2006)* Mello (1999)* Mello et al. (2010)* Melotti (2009) Mesquita et al. (2008) Mueller (2014)* Nepomuceno (2005)* Peteleiro et al. (2012) Pinheiro Filho et al. (2012)* Pires et al. (2010) Possas (2001)* Possas and Dweck (2006)* Saraiva (2012)* Silva et al. (2007)* Soares-Filho et al. (2002)* Strauss and Borenstein (2010) Tabak et al. (2009)* Tabak et al. (2009b)* Takahashi et al. (2008)* Vasconcellos (2013)* * Preliminarily compiled by Bernardo Mueller. Applications in Brazil, Product 1 of the project Modeling Complex Systems for Public Policies Public Health Epidemics and Health ABM Analytical Environment CA MAS CA Migration Crime ABM ABM Social Social Education Movements Machine Dynamic Network Network Analysis Learning Systems Analysis 23
41 Complexity Centers At least, 6 specific academic journals + papers in Nature, Science, MIT review, and American Economic Review Applications world 24
Why Public Policies, then? 25
Agents are heterogeneous Everything is interconnected... and it matters: the whole is more than the sum of the parts Cross-effects, influence of policies Multiplicity of sectors, scales Multiplicity of models and methodologies Anecdote: cow weight (Page) Public Policies 26
Nicolescu (1999): 1. (non) existence general laws 2. Use of experiments (decode laws) 3. Irreplicability; probabilistic Public Policies, social sciences I 27
Discontinuities, ruptures; Unique, discrete events; Uncertainties Public Policies, social sciences II 28
1. Understand underlying mechanisms 2. Identify key-elements (actors, tipping points) 3. Improve resilience (of the system), decrease vulnerabilities Thus: 29
Including elements of CS approach Formally Simulating scenarios, prognosis Cheaply, repeatedly, (relatively) easily Multiple models Communicating Intensive use of data Modeling 30
it is not uncommon for small changes to have big effects; big changes to have surprisingly small effects; and for effects to come from unanticipated causes OECD, 2009 31
Thanks 32