Tuesday, May 26, 2015

A Semester with Spatial Agent-based Models

With the spring semester over, I thought I would show some of the final agent-based modeling projects that were carried out in CSS 645: Spatial Agent-based Models of Human-Environment Interactions. As always I was quite impressed the models and we had a plethora of topics ranging from mobile agent-based models, shopping, pick pocketing, route finding, travel to work, the spatial spread of information, deer management, urban growth etc... What is interesting is that while the majority of models are implemented in NetLogo, more and more are being done in Python.

Something new for this semester is we also tried to reproduce a published model. Below you can see 3 examples of such work. Click here to see a previous post on reproduction and replication. 

Wednesday, April 22, 2015

Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges

This week I am attending the AAG Annual Meeting in Chicago. While here, we organized 3 sessions entitled "Geosimulation and Big Data: A Marriage made in Heaven or Hell?" in which I presented a paper, co-authored with Sarah Wise: "Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges." The abstract is below:
The rise of crowdsourcing has made new kinds of data available to the  geographical community. New forms of data range in their characteristics and purpose. One example is Volunteered Geographical Information (VGI), were users purposely contribute Geographic Information (GI) as in the case of OpenStreetMap; another is Ambient Geographic Information (AGI), where the intention of contributors is not necessarily to provide GI, but GI can be derived, as from Twitter. While much progress has been made in utilizing these new sources of data in GIScience, they have only recently begun to be integrated into agent-based models (ABM). This paper will discuss the opportunities that crowdsourced data provides for ABMs, specifically focusing on how such information gives us a new lens to study the micro-interactions of individuals. Through as series of examples we will demonstrate how such data can be integrated into geographically explicit ABMs. By building on these examples we will showcase how the spatial environment and agent populations can be built using crowdsourced information, and highlight how agent behaviors can be informed and validated by such information. We further discuss the challenges associated with this program of research: using such data is not without its difficulties, including gathering or accessing the data, storing the data, analyzing the collected data, and assessing its validity. Together, this work provides a brief overview of the current state of crowdsourced data-informed ABM. 
If you like what is written above, you can have a flick through the slides from the talk or check out one of the movies:

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.