Tuesday, March 31, 2015

Exploring Creativity and Urban Development with Agent-Based Modeling

There is considerable debate about "creative cities" and relatively few agent-based models that explore such ideas from the bottom up. To that end we have recently published a paper in the Journal of Artificial Societies and Social Simulation entitled: "Exploring Creativity and Urban Development through Agent-Based Modeling"

In the paper we introduce the Creative City Model, an exploratory ABM to simulate the theoretical relationship between land-use regulation, urban mobility and societal tolerance on the economic performance of cities. The model is based on simplified assumptions from our empirically informed understanding of urban morphology, economic geography and the diffusion of creativity from human interactions.  It contributes to the growing literature exploring the dynamic socioeconomic processes underlying urban economic growth through computer simulation. Specifically the model offers a new lens to view the diffusion of creativity through knowledge spillovers under various scenarios from the bottom up. Through experimentation, the model suggests the existence of tradeoffs between the desire for social equity, estimated via rent affordability, and the rapid diffusion of creativity. Below you can find the abstract of the paper.

Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socioeconomic diversity and societal tolerance; the elevated interaction intensity improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public service standards. We further explore these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of scenarios resulting in conditions of intense human interaction results in greater economic growth. However, model results also demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity. 

Key Words: Agent-Based Modeling; Developing Countries; Urban; Segregation; Land-use; Transportation
The movie below shows a typical simulation run of the model.

Further details about the model along with its ODD is available from the OpenABM website (click here).

Full Reference:
Malik, A.A., Crooks, A.T., Root, H.L. and Swartz, M. (2015), Exploring Creativity and Urban Development through Agent-Based Modeling, Journal of Artificial Societies and Social Simulation. 18 (2): 12. Available at http://jasss.soc.surrey.ac.uk/18/2/12.html

Friday, March 27, 2015

Bumble Bee Colonies

When reading papers about agent-based models / individual-based models, I am always curious if the model can be reproduced from the description in said paper. Specifically whether there is sufficient information in the paper to reproduce the model and the results. Often we task students with such a task as a learning exercise and I thought it would be nice to show you one such example done by Dale Brearcliffe.

The model that was reproduced is entitled "The Ontogeny of the Interaction Structure in Bumble Bee Colonies: A MIRROR Model" by Hogeweg and Hesper (1983) which explored whether or not an individual-oriented model of population dynamics and simple bumble bee behaviors could produce the ontogeny of the social interaction of the colony? The original model used MIRSYS, a program written   in INTERLISP. Dale was able to take the paper and reproduce the model using NetLogo, but not replicate the results (click here to read more and download the model).

Graphical User Interface of Reproduced  Model

Nest composition and development in the original model (Source: Hogeweg and Hesper, 1983).
Nest composition and development in the reproduced model.

Full Reference:
Hogeweg, P. and Hesper, B. (1983), 'The Ontogeny of the Interaction Structure in Bumble Bee Colonies: A MIRROR Model', Behavioral Ecology and Sociobiology, 12(4): 271-283. 

For those interested in reproduction and replication have a look at the following articles:
Axtell, R., Axelrod, R., Epstein, J.M. and Cohen, D. (1996), 'Aligning Simulation Models: A Case Study and Results', Computational and Mathematical Organization Theory, 1(2): 123-141. 
Drummond, C. (2009), 'Replicability is Not Reproducibility: Nor is it Good Science', Proceedings of the 4th Workshop on Evaluation Methods for Machine Learning at the 26th International Conference on Machine Learning, Montreal, Canada.
Wilensky, U. and Rand, W. (2007), 'Making Models Match: Replicating an Agent-Based Model', Journal of Artificial Societies and Social Simulation, 10(4): 2, Available at http://jasss.soc.surrey.ac.uk/10/4/2.html.

Thursday, March 26, 2015

Collective Behavior of In-group Favoritism

We just had a paper accepted in Advances in Complex Systems entitled "The Effect of In-group Favoritism on the Collective Behavior of Individuals' Opinions." In the paper we develop and an agent-based model to explore how individuals interact and how more  collective behaviors emerge (e.g. reaching a consensus or the spreading of opinions). The abstract of the paper is as follows:

Empirical findings from social psychology show that sometimes people show favoritism toward in-group members in order to reach a global consensus, even against individuals' own preferences (e.g., altruistically or deontically). Here we integrate ideas and findings on in-group favoritism, opinion dynamics, and radicalization using an agent-based model entitled cooperative bounded confidence (CBC). We investigate the interplay of homophily, rejection, and in-group cooperation drivers on the formation of opinion clusters and the emergence of extremist, radical opinions. Our model is the first to explicitly explore the effect of in-group favoritism on the macro-level, collective behavior of opinions. We compare our model against the two-dimentional bounded confidence model with rejection mechanism, proposed by Huet et al. (2008), and find that the number of opinion clusters and extremists is reduced in our model. Moreover, results show that group influence can never dominate homophilous and rejecting encounters in the process of opinion cluster formation. We conclude by discussing implications of our model for research on collective behavior of opinions emerging from individuals' interaction. 
 Keywords: Opinion dynamics; in-group favoritism; homophily; radicalization; extremism.

Full reference:
Alizadeh, M., Cioffi-Revilla, C. and Crooks, A.T. (2015), The Effect of In-group Favoritism on the Collective Behavior of Individuals' Opinions, Advances in Complex Systems. DOI: 10.1142/S0219525915500022 (pdf)
The code for the model is available from here.

Friday, March 20, 2015

Lipari School on Computational Social Science

If you are wondering what to do between July 26 and August 1, this summer, you might be interested in this years Lipari School on Computational Social Science which is focusing on Algorithms, Data, and Models for Social and Urban Systems

What will be taught at the summer school and why? To answer the these questions and to quote from the the homepage of the school:
 "Social and urban systems have been the focus of social science theory and research for centuries, but only until recently have computational approaches enabled novel explorations of challenging and enduring research questions and the opening of new frontiers for investigation. What is the role of Computational Social Science in advancing the science of social and urban systems? Which advanced algorithms and data structures play a key role in these investigations? In 2015 our Lipari Summer School in CSS will address questions such as the role of GIS (geographic/geospatial information systems), social media, big social data, agent-based models, network models, and their integration in the study, design, and implementation of social and urban systems. "
The speakers will be:
Special guest speakers will be:
To find out how to apply to attend the summer school click here. Students are encouraged to apply early because enrollment is competitive and limited.

Thursday, February 12, 2015

Village Model

What now seems like a very long long time ago, when I was getting up to speed with Agent-based modeling and GIS, I came across a great edited book entitled "Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes". 

One chapter in particular that I really enjoyed because of its clarity and use of data was by Kohler et al. (2000) entitled "Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest". 

The chapter explored the question of why did Pueblo people vary their living arrangements between  compact villages and dispersed hamlets between 901-1287AD? To this day, I use this chapter when I am teaching about early agent-based models. While the initial model was implemented in Swarm, it has now been ported to Repast and developed further by an NSF supported program called Village Ecodynamics Project.

Full Reference:
Kohler, T.A., Kresl, J., Van Wes, Q., Carr, E. and Wilshusen, R.H. (2000), 'Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest', in Kohler, T.A. and Gumerman, G.J. (eds.), Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, Oxford University Press, Oxford, UK, pp. 145-178.