Tag Archives: Analysis

Micro-dynamics of Reciprocity in an Asymmetric Conflict (Updated)

Thomas Zeitzoff is a PhD candidate at New York University. I came across his research thanks to the Ushahidi network, and am really glad I did. He wrote a really neat paper earlier this year on “The Micro-dynamics of Reciprocity in an Asymmetric Conflict: Hamas, Israel, and the 2008-2009 Gaza Conflict,” which he is revising and submitting to peer-review.

Updated: Thomas kindly sent me the most recent version of his paper which you can download here (PDF).

I’ve done some work on conflict event-data and reciprocity analysis (see this study of Afghanistan), but Thomas is really breaking new ground here with the hourly temporal resolution of the conflict analysis, which was made possible by Al-Jazeera’s War on Gaza project powered by Ushahidi.

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Abstract

The Gaza Conflict (2008-2009) between Hamas and Israel was de fined the participants’ strategic use of force. Critics of Israel point to the large number of Palestinian casualties compared to Israelis killed as evidence of a disproportionate Israeli response. I investigate Israeli and Hamas response patterns by constructing a unique data set of hourly conflict intensity scores from new social media and news source over the nearly 600 hours of the conflict. Using vector autoregression techniques (VAR), I fi nd that Israel responds about twice as intensely to a Hamas escalation as Hamas responds to an Israeli escalation. Furthermore, I find that both Hamas’ and Israel’s response patterns change once the ground invasion begins and after the UN Security Council votes.

As Thomas notes, “Ushahidi worked with Al-Jazeera to track events on the ground in Gaza via SMS messages, email, or the web. Events were then sent in by reporters and civilians through the platform and put into a Twitter feed entitled AJGaza, which gave the event a time stamp. By cross-checking with other sources such as Reuters, the UN, and the Israeli newspaper Haaretz, I was able see that the time stamp was usually within a few minutes of event occurrence.”

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Key Highlights from the study:

  • Hamas’ cumulative response intensity to an Israeli escalation decreases (by about 17 percent) after the ground invasion begins. Conversely, Israel’s cumulative response intensity after the invasion increases by about three fold.
  • Both Hamas and Israel’s cumulative response drop after the UN Security Council vote on January 8th, 2009 for an immediate cease-fi re, but Israel’s drops more than Hamas (about 30 percent to 20 percent decrease).
  • For the period covering the whole conflict, Hamas would react (on average) to a “surprise” 1 event (15 minute interval) of Israeli misinformation/psy-ops with the equivalent of 1 extra incident of mortar re/endangering civilians.
  • Before the invasion, Hamas would respond to a 1 hour shock of targeted air strikes with 3 incidents of endangering civilians. Comparatively, after the invasion, Hamas would only respond to that same Israeli shock with 3 incidents of psychological warfare.
  • The results con rm my hypotheses that Israel’s reactions were more dependent upon Hamas and that these responses were contextually dependent.
  • Wikipedia’s Timeline of the 2008-2009 Gaza Conflict was particularly helpful in sourcing and targeting events that might have diverging reports (i.e. controversial).

Perhaps the main question I would have for Thomas is how he thinks his analysis and findings could be used for conflict early warning and rapid response to violent conflict.

Patrick Philippe Meier

How Ushahidi Can Become a Real Early Warning Platform

Ushahidi currently uses incident reporting (or more technically event-logging) as the methodology to document violent events that have taken place. While Ushahidi’s use of FrontlineSMS accelerates the crowdsourcing of crisis information, the violence reported on Ushahidi has by definition already occurred and thus cannot be prevented.

UshahidIncRep

This blog post is the first of a two-part series on taking Ushahidi to the next level. The second part in this series  addresses “How Ushahidi Can Become a Real Early Response Platform.”

Current Setup

Ushahidi’s use of SMS to crowdsource crisis information means that violent incidents can be reported in quasi real-time, a significant advantage over traditional conflict early warning systems such as the Horn of Africa’s Conflict Early Warning and Response Network (CEWARN) and the now defunct FAST Early Warning System by Swisspeace.

To be sure, the use of SMS and geo-tagging means that escalating violence can be documented as it happens and where it happens. This can alert organizations of the need to contain or prevent further bloodshed. However, as has been argued by scholars and practitioners, as more blood is spilled the probability of reversing the violence without military intervention grows slim.

Situation Reports

The more robust applied social science methodologies I have come across for field-based conflict early warning combine both incident and situation reporting. Think of them as two types surveys. While the list of indicators in incident reports (IncRep) comprises violent events that seek to be prevented, the list of indicators in situation reports (SitReps) comprises events that are thought to render IncRep indicators more likely.

In other words, if event E is listed on an IncRep, the corresponding SitRep would include events E – t, i.e., those events that usually precede the violent event E in time. These SitRep events can be political, social, economic, ecological, historical etc., and should be grouped into such categories.

However, events E – t that tend to mitigate or prevent events E should also be included in SitReps. We want to know what is going right in order to identify existing entry points for conflict management. Violent conflict is never total in the Clausewitzian sense of total war. There are always pockets of cooperation and intervention. These, however indirect, need to be identified and understood.

SitReps are completed periodically, e.g., on a weekly basis, unlike IncReps, which are completed episodically, i.e., as incidents of violence take place. In other words, regular and consistent situation reporting should be encouraged. SitRep events should be formulated as indicators framed as questions.

For example, “Are university students having their freedom of speech curtailed?” would imply that a regime has acted (an event) to restrict student behavior. This act could elicit student protests and thence “a violent crackdown by government forces,” which would be an IncRep indicator.

cewarnSitRep

SitRep surveys should also use a Likert scale, i.e., answers to SitRep indicator questions should range from “strongly disagree” to “strongly agree.” This means the answers can be weighted. See the CEWARN screenshot above for an operational example of a situation report.

Rationale

Instead of only monitoring incident reports, i.e., violence that has already come to pass, Ushahidi users would be able to monitor and analyze the causal factors themselves to determine whether they are increasing in number and intensity. This could signal potential early warning signs before the causal factors translate into violent events.

As more IncReps and SitReps are completed, users can also empirically assess which SitRep indicators act as the real triggers (and “preventers”) of the violent events documented in IncReps. In addition, after weekly SitReps are completed over several months, they can be aggregated to form a baseline for what the “average” week looks like.

Baseline Analysis

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This means that users could then compare each new situation report with the baseline as depicted above. Inflection points, the doted circles, are points of interest since they denote change in trends. The dotted lines represent the threshold beyond which an organization will intervene. Alerts 1 and 2 signal that the threshold has almost been reached.

The important point to note is that baseline analysis of SitRep indicators can identify when the causes of violent conflict are increasing in intensity and frequency. Furthermore, patterns might be identifiable in the causes of episodic conflict and these could inform structural prevention strategies as well as conflict sensitive programming.

Next Steps

It would be great for Ushahidi to at least provide users with the option of setting up their own Situation Report form. I would also recommend that Ushahidi automatically flag when thresholds are crossed. In addition, Ushahidi should integrate some basic statistical techniques so that SitRep indicators (causes and preventers of conflict) could be automatically flagged when they are statistically correlated with IncRep indicators, i.e., violent events.

The purpose of including SitReps in Ushahidi is to encourage evidence-based programming and make early warning less of a hypothetical possibility.