This is a guest post written by Matt Reichert, Miguel Garces, Quratul-Ann Malik, and Ian Lustick of Lustick Consulting. Any questions about this post, these models, Lustick Consulting, or agent-based modeling can be directed to info@lustickconsulting.com.
For students of civil-military relations, the ouster of Ukrainian ex-President Viktor Yanukovich is puzzling—it’s just not quite clear what to call it. Unlike the case of Egypt, where the checklist of coup criteria can be marked down with a single swift sentence, classifying the Ukraine case has required much more thought. With parliament as the chief perpetrator, and without the Ukrainian armed forces or military playing a significant role, this coup feels like a fire without the smoke. It is not so much a coup, as coup-ish.
How do we forecast “coup-ish?” We suggest that the Ukraine case typifies the type of forecasting challenge that is best met by conceptual disaggregation. In his treatment of Ukraine, what Ulfelder has captured is the unique analytic role played by what has been termed “diminished subtypes.” According to David Collier and Steven Levitsky (1997), a diminished subtype appends and subtracts one or more criteria from some root concept, making it related to, but not quite a pure specification of, that concept. It is the root concept “with adjectives.” Their example is ‘illiberal democracy,’ which does not meet the minimal requirements of the root concept ‘democracy,’ and includes additional criteria and a smaller number of cases than the root concept democracy. Yet there is still important analytic utility to defining the concept in connection with democracy.
What happened in Ukraine might be classified not as a pure ‘coup,’ yet for Ulfelder, it is clearly close enough that recognizing it as ‘coup-ish’ in some way is analytically useful. Thus, we propose the diminished subtype ‘parliamentary coup.’
For this to hold, the diminished subtype ‘parliamentary coup’ must satisfy most, break at least one, and add at least one criterion to the existing definitional criteria for the root concept “coup.” To understand how this might be accomplished, it is useful to first re-state the three coup definitions used by Ulfelder – one each from the coding rules for the two datasets used to validate his coup forecasts, and one from Ulfelder’s own commentary on Ukraine – and identify where they are congruent and diverge. Following the example of Powell and Thyne, we break down the definitions into three components.
|
Target |
Perpetrator |
Tactics |
|
| Powell & Thyne (Ground truth A) |
Chief executive | Any elite who is part of the state apparatus | Illegal; no minimal death criteria |
| Marshall & Marshall (Ground truth B) |
Executive authority | A dissident/opposition faction within the country’s ruling or political elites | A forceful seizure |
| Ulfelder (Ukraine commentary) |
Chief executive | Political insiders | Do not follow constitutional procedure; involves use or threat of force |
There is general agreement here on the target, chief executive, and some agreement on the tactic—it must be extra-legal and in some way forceful. There is disagreement on the perpetrator—whether it must be a state actor, or simply a political insider.
Another way of conceptualizing these definitions and their inter-relationships is that extra-legal seizure of the chief executive office operates as an overarching abstract concept. By specifying the perpetrator, we build the root concept ‘coup:’ an extra-legal seizure of the chief executive office by some state actor. When we adjust the specification of the perpetrator, we get our diminished subtype ‘parliamentary coup:’ an extra-legal seizure of the chief executive office by an unarmed political insider. The diminished subtype shares but also excludes some criteria of the root concept, and both fit comfortably under the over arching abstract concept, as illustrated in the figure below.
We included here the subtype ‘military coup’ to distinguish the conventional tactic of specification from the diminished subtype route. While the diminished subtype satisfies some but not all of the criteria of the root concept, the conventional subtype exists entirely within the root concept, and simply drops the level of generality by adding criteria and reducing the number of qualifying cases. Thus, while all military coups are coups, not all coups are military coups – and all parliamentary coups are not quite coups, but still coup-ish.
What does classifying our classification scheme buy us? The added value is more than semantic. How we choose to conceptualize, and being self-aware about those choices, can shape and affect how we analyze, how we model, and also how we forecast. As Giovanni Sartori observed (1970), “concept formation stands prior to quantification.” Here, we will demonstrate how using these different conceptualizations in a model, and taking on the analytic baggage that comes with each, affects not just the forecasts we make, but also the questions we ask about those forecasts.
An agent-based model makes for especially fertile ground for this type of introspective test, for two reasons. First, it is a causal model, which means that the individual chains of implications for each type of concept are available for interrogation. Second, the animating principle behind the agent-based modeling approach is complexity, which incorporates the overlapping chains of causation and nth order effects produced by thousands of individually specified interacting agents. This means that the ultimate effects of specifying a concept one way or another are truly unpredictable at the outset.
Here, we will test the forecasting implications of these alternative conceptualizations of a coup, using the Virtual Strategic Analysis and Forecasting Tool (V-SAFT). V-SAFT, which Lustick Consulting has developed with support from DARPA and ONR, includes a battery of agent-based models representing virtual countries available for interrogation, experimentation, and forecasting. Each model is comprised from (1) a landscape of individual agents heterogeneously characterized to reflect the particular social and political topology of the target country, (2) simple rules of interaction by which agents adopt, discard, and trade politically motivating identities, and (3) broader rules orienting swaths of the landscape toward or away from the political center of power (classified as levels, from ‘dominance’ at the center, to a position of radical opposition at the ‘non-system’ level).
Using our models for Thailand, Pakistan, and Egypt, and keeping with the examples posed above, we operationalized a coup in three ways.
| Concept | Definitional Criteria | Model Operationalization |
| A Coup (root concept) |
Extra-legal seizure | A move from the ‘non-system’ level of the model |
| Of the chief executive office | To a position of political ‘dominance’ in the model | |
| By any state actor | By the ‘state’ OR the ‘military’ identity | |
| A Military Coup (subtype) |
Extra-legal seizure | A move from the ‘non-system’ level of the model |
| Of the chief executive office | To a position of political ‘dominance’ in the model | |
| By a military actor | By the ‘military’ identity exclusively | |
| A Parliamentary Coup (diminished subtype) |
Extra-legal seizure | A move from the ‘non-system’ level of the model |
| Of the chief executive office | To a position of political ‘dominance’ in the model | |
| By any unarmed political insider | By any ‘political party’ identity, but NOT the state or military identities |
Our forecast for each conceptualization of a coup, alongside Ulfelder’s original forecasts, appear in the figure below.
A first glance tells us that disaggregating the root concept ‘coup’ affects the rank ordering of likelihoods. With the parliamentary coup (diminished subtype) operationalization, the rankings produced by LC’s forecasts match Ulfelder’s. With the simple coup or military coup (subtype) operationalizations, however, Thailand drops in the rankings, behind Pakistan and then Egypt.
Where this kind of concept disaggregation buys us the most explanatory power is where we see forecast divergence. In Ulfelder’s forecast, we see the greatest disagreement between his two models on the forecast for Thailand, indicating a lower level of certainty for forecast accuracy. In LC’s forecasts, we also see the greatest disagreement in Thailand. In other words, how we operationalize and disaggregate the root concept ‘coup’ seems to have the greatest implications in Thailand, compared to Pakistan and Egypt – especially with regard to the diminished subtype. What are the implications in Thailand of such a jump in likelihood for a parliamentary coup, alongside a decrease in the likelihood for a military or traditional coup?
One of the more puzzling features of the Ukraine case’s coup-ish-ness is its normative implications. If the Ukrainian case is a ‘just coup,’ is that exceptional, or are all parliamentary coups ‘just?’ And would a different type of coup have been unjust? The figure below provides some insight. Here we see the likelihood of subversive anti-state violence—a dependent variable endogenously produced by V-SAFT models—disaggregated by coup type. While the presence of a coup significantly increases the likelihood of anti-state violence, the effect is generally greater for a traditional or military coup. The relationship between coup type and violence is more pronounced in the Thailand case, which is likely due to its larger sample size (as noted in the preceding figure, Thailand generated more parliamentary coups). So, while coups of any kind are rarely peaceful, the diminished subtype that we saw in Ukraine is generally associated with less popular violence. In this way, disaggregating our concept can at times help our analytical thinking inform our moral thinking.
By systematically exploiting theoretically differentiated ABM simulation models, forecasters and analysts can sharpen their self-awareness of concept choice, measure its implications, and ultimately improve the real utility of concept categories for policy makers. To further explore the forecasts produced by V-SAFT’s regular battery of country models (Egypt, Pakistan, Thailand, the Philippines, Bangladesh, Venezuela, Indonesia, Malaysia, and Kenya), please visit this page (registration required) on Lustick Consulting’s web site. For our publications on the application of agent-based modeling approaches to social science, please see this page (no registration required).
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