Tag Archives: Crisis Simulation

Crisis Mapping and Agent Based Models

The idea of combining crisis mapping and agent based modeling has been of great interest to me ever since I took my first seminar on complex systems back in 2006. There are few studies out there that ground agent based models (ABM) on conflict dynamics within a real-world geographical space. One of those few, entitled “Global Pattern Formation and Ethnic/Cultural Violence,” appeared in the journal Science in 2007.

Note that I take issue with a number of assumptions that underlie this study as well as the methodology used. That said, the study is a good illustration of how crisis mapping and ABM can be combined.

Introduction

The authors suggest that global patterns of violence arise due to “the structure of boundaries between groups rather than the groups themselves.” In other words, the spatial boundaries between different populations create a propensity for conflict, “so that spatial heterogeneity itself is predictive of local violence.”

The authors argue that this pattern is “consistent with the natural dynamics of “type separation,” a specific pattern formation also observed in physical and chemical phase separation. The unit of analysis in this study’s ABM, however, is the local ethnic “patch size,” which represents the smallest unit of ethnic members that act collectively as one.

The Model

A simple model of type separation assumes that individuals (or ethnic units) prefer to move to areas where more individuals of the same time reside. Playing the ABM yields progressively larger patches or “islands” of each ethnic group over time. The relationship between patch size and time follows a power law distribution, “a universal behavior that does not depend on many of the details of the model […].”

In other words, the model depicts scale invariant behavior, which implies that “a number of individual agents of the model can be aggregated into a single agent if time is rescaled correspondingly without changing the behavior at the larger scales.”

To model violent conflict, the authors assume that both highly mixed regions and well-segregated groups do not engage in violence. The rationale regarding the former being that in highly mixed regions, “groups of the same type are not large enough to develop strong collective identities, or to identify public spaces as associated with one or another group. When groups are much bigger, “they typically form self-sufficient entities that enjoy local sovereignty.”

To this end, the authors argue that partial separation with poorly defined boundaries fosters conflict when groups are of a size that allows them to impose cultural norms on public spaces, “but where there are still intermittent violations of these rules due to the overlap of cultural domains.” In other words, conflict is a function of population distribution and not of the “specific mechanism by which the population achieves this structure, which may include internally or externally directed migrations.”

The model is therefore founded on the principle that the conditions under which violent conflict becomes likely can be determined by census.

The Analysis

The authors used 1991 census data of the former Yugoslavia and the Indian census data from 2001 and converted the data into map form (see figure below), which they used in an ABM simulation. “Mathematically, the expected violence was determined by detecting patches consisting of islands or peninsulas of one type surrounded by populations of other types.”

mexicanhat

A wavelet filter that has a positive center and a negative surround (also called a Mexican hat filter) was used to detect and correlate the islands/peninsulas. scienceabm1

The red overlays depicted in Figure D above represents the maximum correlation over population types. The diameter of the positive region of the wavelet, i.e., “the size of the local population patches that are likely to experience violence,” is the main predictor of the model.

scienceabm2

To test the predictive power of their model, the authors compared the locations of red overlays with actual incidents of violence as reported in books, newspapers and online sources (the yellow dots in the crisis map below).

yugoabm

Their statistical results indicate that the Yugoslavia crisis map model has a correlation of 0.89 with reports. Moreover, “the predicted results are highly robust to parameter variation [patch size], with essentially equivalent agreement obtained for filter diameters ranging from 18 to 60 km […].”

The statistical results for the India crisis map model indicate a correlation of 0.98. The range of the patch size overlapped that of the former Yugoslavia but is shifted to larger values, up to 100km. This suggests that “regions of width less than 10km or greater than 100km may provide sufficient mixing or isolation to reduce the chance of violence.”

Conclusion

While the authors recognize the importance of social and institutional drivers of violence, they argue that, “influencing the spatial structure might address the conditions that promote violence described [in this study].” In sum, they suggest that, “peaceful coexistence need not require complete integration.”

What do you think?

Patrick Philippe Meier

iRevolution One Year On…

I started iRevolution exactly one year ago and it’s been great fun! I owe the Fletcher A/V Club sincere thanks for encouraging me to blog. Little did I know that blogging was so stimulating or that I’d be blogging from the Sudan.

Here are some stats from iRevolution Year One:

  • Total number of blog posts = 212
  • Total number of comments = 453
  • Busiest day ever = December 15, 2008

And the Top 10 posts:

  1. Crisis Mapping Kenya’s Election Violence
  2. The Past and Future of Crisis Mapping
  3. Mobile Banking for the Bottom Billion
  4. Impact of ICTs on Repressive Regimes
  5. Towards an Emergency News Agency
  6. Intellipedia for Humanitarian Warning/Response
  7. Crisis Mapping Africa’s Cross-border Conflicts
  8. 3D Crisis Mapping for Disaster Simulation
  9. Digital Resistance: Digital Activism and Civil Resistance
  10. Neogeography and Crisis Mapping Analytics

I do have a second blog that focuses specifically on Conflict Early Warning, which I started at the same time. I have authored a total of 48 blog posts.

That makes 260 posts in 12 months. Now I know where all the time went!

The Top 10 posts:

  1. Crimson Hexagon: Early Warning 2.0
  2. CSIS PCR: Review of Early Warning Systems
  3. Conflict Prevention: Theory, Police and Practice
  4. New OECD Report on Early Warning
  5. Crowdsourcing and Data Validation
  6. Sri Lanka: Citizen-based Early Warning/Response
  7. Online Searches as Early Warning Indicators
  8. Conflict Early Warning: Any Successes?
  9. Ushahidi and Conflict Early Response
  10. Detecting Rumors with Web-based Text Mining System

I look forward to a second year of blogging! Thanks to everyone for reading and commenting, I really appreciate it!

Patrick Philippe Meier

Humanitarian Assistance Training Simulator

Howard Rheingold, who is on my “shadow dissertation committee”, recently flagged this very neat training simulator called Virtual Peace for students interested in humanitarian response. The platform brings together digital learning technologies and serious gaming for humanitarian aid education.

VP6

Students and educators enter an immersive, multi-sensory game-based environment that simulates real disaster relief and conflict resolution conditions in order to learn first-hand the necessary tools for sensitive and timely crisis response. [...] The simulation developed by these partners takes as its model the real-life events following a major natural disaster: Hurricane Mitch, which devastated much of Central America in 1998.

VP2

When playing the game, students who represent governments, the United Nations (UN) and nongovernmental organizations, meet in the Virtual Peace simulation. As part of the simulation, students are responsible for providing and coordinating aid to the two countries hardest hit by Hurricane Mitch: Honduras and Nicaragua. Students carry out background research on the scenario in order to represent one of 16 different humanitarian organizations such as Medecins Sans Frontieres (MSF), the Office of Foreign of Affairs of Nicaragua, USAID and the World Health Organization (WHO).

By playing the game together, the students are responsible for navigating the challenges and pathways to effective response. Each student assumes her or his role as an avatar designed to look like actual persons in the international aid community. They converse via voice and text channels in the game space which is designed to look like the real venues where these representatives might convene. Students present their views to the group at large and then (within the virtual space) break out into smaller groups for planning and negotiation.

VP3

In addition to allowing students to use real life diplomatic and conflict resolution skills, students and teachers can bookmark specific instances in the game that can be referred back to for learning purposes when debriefing the simulation. This allows them to assess whether certain goals were met, if students properly represented the values and views of their government or organization and how they can work more effectively in the future.

VP5

By extending to educators the multi-sensory nature of the digital media, Virtual Peace becomes much more than just an extension of a role-playing exercise. Ultimately, it is a cutting edge interdisciplinary platform for creative learning, uniting the technologies of the future and the experiences of the past. For more on virtual training for humanitarian response, see my blog post on 3D Crisis Mapping for Disaster Simulation Training.

I like the fact that Virtual Peace has “transformed video game technology previously used for army training simulations into an innovative tool for international humanitarian aid education.” In addition, I like the combination of the serious game with the social networking platform, Ning. This would be a neat addition to the Humanitarian Studies Initiative (HSI) seminar co-taught by the Harvard Humanitarian Initiative (HHI).

Indeed, I wish Virtual Peace had been around in 2006 when I taught an undergraduate course on “Disaster and Conflict Early Warning Systems” because Duke University actually offers a seminar on “Policy Anlaysis for Development” that uses Virtual Peace as a pedagogical tool.

The pictures and accompanying text above were taken from this Virtual Peace video demo:

Patrick Philippe Meier