Modelling innovation diffusion: One method, a few approaches to validation, and several problems. Andreas Ernst

Transcription

1 Modelling innovation diffusion: One method, a few approaches to validation, and several problems Andreas Ernst Center for Environmental Systems Research (CESR) University of Kassel, Germany Workshop on Social unrest among humans and animals, Wageningen UR, March 18, 2014 Spatial spreads of behaviour Innovation diffusion as spreading behaviour Generalizable features of behavioural diffusion phenomena Emergence and micro-foundation Heterogeneity of actors: Preferences, lifestyles, resources, social connectedness, Path dependency Processes: Individual information processing and deciding (mostly in humans ) Social influence as a most important determinant in adoption of behaviour Spatial proximity of actors 1

2 Case study: Elektrizitätswerke Schönau (EWS) Total number of green electricity customers Fukushima Source: own figure, based on original data 3 Spatial diffusion of EWS green electricity November 2003 November

3 Modelling of citizen agents Innovation modelling needs credible citizen agents Very high number of heterogeneous actors Psychologically plausible decision processes, derived from empirical work in social and cognitive psychology, cognitive science Geographically explicit mapping Dynamic social networks Lightweight Architecture for bounded Rational citizen Agents (LARA) Fills a gap between Frameworks with no built-in psychological foundations (like Repast) and Full-fledged cognitive architectures like ACT-R or SOAR Provides prefabricated components like perception, memory, modes of decision making, adaptation algorithms, social influence lara-framework.sourceforge.net 3

4 Modular structure Change in environment(s) due to own actions LARA: The general perception-action loop (Briegel, Ernst, Holzhauer, Klemm, Krebs & Martínez, 2012) Perception Memory Pre-processor Preference structure: goals (motives, value, need satisfaction, orientors, ideals) Goal weights Behavioural options Action selection and learning Post-processor Action Other changes in the environment(s): Innovation characteristics Legal and other constraints Social networks Goals Infrastructure Budget Behavioural options History Chooses habitual, heuristic, or deliberative action path Preselection of action options on the basis of current situational context E.g. innovation adoption, spread of information Storing Biasing, if applicable (Acquired) Knowledge about usefulness of behavioural options for achieving goals Current goal preferences Situational influences on goals Basic (long-term) orientations Goal 1 Goal 2 Goal n Importance Importance Behavioural option Goal Goal Behavioural option Goal Goal Behavioural option m Goal n Goal n 0.8 Current usefulness of behavioural options for achieving goals Goal 1 Goal 2 Goal n Behavioural option Behavioural option Behavioural option m Attainability of one goal with the current behavioural options, Overall usefulness of one behavioural option to achieve goals... Freedom of action: Current efficacy of overall goal satisfaction 4

5 Implementation of LARA (Briegel, Ernst, Holzhauer, Klemm, Krebs & Martínez, 2012) Principles: Flexibility, light weight, performance Event bus Supports flexible flow control and state-dependent activation Ordering of component execution Every component subscribes at the event-bus for a certain event, if applicable After publication of that event by another component, the event bus triggers the instances have subscribed for that event Currently working on the implementation of a general concept of parallel execution using the event bus Implemented in Java Embedding of LARA Social Bio-Physical Socio-Economic External Controller (e.g. Repast) LARA Visualisation GIS, Repast, etc. Analysis DB, R, etc. 5

6 Lifestyles: Introducing heterogeneity Social status and basic values Higher 1 Conservatives 5% Etablisheds 10% Leading Milieus Post- Materialists Post-Materialists 10% Modern Performers 9% Middle Lower 2 3 Traditionals 14% GDR- Nostalgia Traditional Milieus 6% Modern Mainstream 16% Mainstream Milieus Consumption Materialists 11% Experimentalists 8% Hedonistic Milieus Hedonists 11% Social Status Basic Values A Tradition Sense of Duty and Order B Modernization Individualization, Self-Actualization, Pleasure C Re-Orientation Multiple Options, Experimentation, Paradoxes Sinus Sociovision 2006 Heterogeneity of agents: two exemplary milieus Post-Materialists Profile: - age = young families - income = middle to high - value modern = high - value conservative = low - importance price = low to middle Post-Materialists Profile: - age = young families - income = middle to high - value modern = high - value conservative = low - importance price = low to middle - importance environment = middle to high - importance peers = low - importance environment = middle to high - importance peers = low Traditionals Profile: - age = older - income = low - value modern = low - value conservative = high - importance price = middle to high - importance environment = low - importance peers = middle to high Traditionals Profile: - age = older - income = low - value modern = low - value conservative = high - importance price = middle to high - importance environment = low - importance peers = middle to high Spatially explicit data of Microm Sinus Sociovision

7 Network generation depending on homophily and distance (Holzhauer, Krebs, & Ernst, 2012) For rings around the agent define p_link that exponentially decreases with distance and ensures that the overall expected number of links is k_milieu For each ring draw k_milieu partners that passed the milieu-specific affiliation probability and apply p_link (baseline homophily) For each created link an additional global partner is added with p_global. That partner needs to respect milieu-specific affiliation preferences (inbreeding homophily). Requires no global knowledge of agents Empirically determined p_links_to (Holzhauer, 2013) 7

8 Sizes of personal networks in different lifestyle groups (Holzhauer, in press) Specifics of the innovation model * Objective of the model Reproduce the diffusion of innovation observed in the EWS data Realise scenarios with a variance of external events, societal constraints, or policy interventions Assumptions Lifestyles have an impact on In-degree and social connectivity preferences Goal preferences Need for cognition: raises the probability of choosing a deliberative decision mode Interventions Raise the number of personal social contacts (e.g. talking to one s neighbours) Media events change the actors goal preferences * Work on the model presented here was supported by the German Federal Ministry of Education and Research (BMBF), grant no. 01UV1003A (SPREAD) 8

9 Further empirical backing Historical development of energy prices Included in the model with step functions of large price increases for conventional (grey) electricity Goal preferences Stem from a large empirical survey and previous projects Goals: Ecology, Cost minimisation, Social Orientation, Reliability of electricity provision Testing the model output to the historically observed diffusion (retrodictive validation) Validation: Proof of Concept Starting with a smaller model region 56,000 agents 250,000 network links Representing 7,000,000 households (at 125 households per agent) Google Maps 9

10 SPREAD model validation measures and procedures* Point of departure Target region comprises 1650 postcode areas Main model output variable for validation is the number of green energy customers in each of the postcode areas. For each postcode area simulations generate time series of 8 simulation years from 2005 to 2012 with monthly resolution, i.e. 96 data points For each of the postcode areas real world data exist showing the number of customers of EWS from 2005 to 2012 with a monthly resolution, 96 data points Rationale of validation exercise Define measures allowing to quantify the fit between model results and real-world data over time Investigate spatial aggregations of the model outputs Start with full aggregation for target area: Investigate time series of 96 time steps (months) obtained by spatially aggregating simulation data and real-world data over all 1650 postcode areas * Special thanks to Friedrich Krebs Comparative assessment of simulation runs Compare a number of simulation runs differing in model parameterisation with respect to 7 indicator dimensions For each simulation run within the ensemble Calculate the 7 indicator values Determine the ranks of the simulation run in the simulation ensemble with respect to each indicator dimension (for indicators which may become negative use absolute value) Perform a linear mapping of the seven ranks to a score value ranging from 0 for the lowest score on the dimension to 100 for the highest score Calculate the total score as the (un-weighted) average of the scores on each of the indicator dimensions Rank simulation runs with respect to the total score 10

11 Ranking of simulation runs runid paramsetid total_score nc_mean_diff_score nc_max_rel_err_score nc_mean_rel_sq_err_score nc_norm_max_rel_err_score nc_norm_mean_rel_sq_err_score incr_mean_diff_score incr_mean_sq_err_score Comparison of spatial results with aggregated ones (Indicator: Mean square error of relative differences over all time steps) 11

12 Discussion The aggregated and spatial errors often match, but not always They hint at different underlying processes producing the errors It is easier to produce a curve fitting the aggregated results over time and that is what most ABMs about spreading of some behaviour do Though: special events (like Fukushima) have to be replicated It is much harder to capture the exact spatial dynamics, driven by Population differences, and Geographic distance from the diffusion The scope of the data the simulation is replicated against is crucial References Briegel, R., Ernst, A., Holzhauer, S., Klemm, D., Krebs, F. & Martínez Piñánez, A. (2012). Socialecological modelling with LARA: A psychologically well-founded lightweight agent architecture. In R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.), Proceedings of the International Congress on Environmental Modelling and Software, Leipzig, Ernst, A. (2003). Agentenbasierte Modellierung des Handelns in Gemeingutdilemmata, Jahrbuch Ökologische Ökonomik, 3, Ernst, A. (2010). Social simulation: A method to investigate environmental change from a social science perspective. In Gross, M. & Heinrichs, H. (Eds.), Environmental Sociology: European Perspectives and Interdisciplinary Challenges (pp ). Berlin: Springer. Holzhauer, S. (in press). Dynamic social networks in agent-based modelling - analysing increasingly detailed approaches of network initialisation and network dynamics. Kassel: University of Kassel (Unpublished dissertation). Holzhauer, S., Krebs, F. & Ernst, A. (2012). Considering baseline homophily when generating spatial social networks for agent-based modelling. Comput Math Organ Theory, Springer, DOI /s Krebs, F., Holzhauer, S. & Ernst, A. (2011). Public Good Provision in Large Populations: The Case of Neighbourhood Support in Northern Hesse. Proceedings of the 7th European Social Simulation Association Conference, Montpellier, France. Schwarz, N. & Ernst, A. (2009). Agent-based modelling of the diffusion of environmental innovations An empirical approach. Technological Forecasting and Social Change, 76, 4,