DOMESTIC STRUCTURE, LEARNING, AND THE DEMOCRATIC PEACE AN AGENT-BASED COMPUTATIONAL SIMULATION

DOMESTIC STRUCTURE, LEARNING, AND THE DEMOCRATIC PEACE: AN AGENT-BASED COMPUTATIONAL SIMULATION A Maurits van der Veen University of Georgia maurits@uga.edu David Rousseau University of Pennsylvania rousseau@sas.upenn.edu August 24, 2004 Draft, not for citation Abstract This paper uses agent-based modeling to study the impact of domestic political structure on the evolution of a democratic peace We show that democratic peace can emerge even with a very limited set of basic assumptions about the relationship between levels of domestic opposition and the costs of initiating conflict In addition, we find that learning among democracies and autocracies alike reduces both the incidence of international conflict and the rate at which the international system consolidates into fewer states Finally, we show that introducing even a fairly weak mechanism for the punishment of ‘pariah’ states (autocracies that attack democracies) suffices to eliminate any semblance of a democratic peace: democracies become more likely to attack not just autocracies but also other democracies To be presented at the annual conference of the American Political Science Association, Chicago, IL, Sept 2004 Domestic structure and the democratic peace Domestic structure, learning, and the democratic peace DOMESTIC STRUCTURE, LEARNING, AND THE DEMOCRATIC PEACE: AN AGENT-BASED COMPUTATIONAL SIMULATION “Democracies don’t attack each other” — Bill Clinton, State of the Union, 1994 "We have no desire to dominate, no ambitions of empire Our aim is a democratic peace" — George W Bush, State of the Union, 2004 Introduction Although fifteen years has elapsed since Levy argued that democratic peace is “the closest thing we have to an empirical law” in international relations (Levy, 1989: 88), the causal mechanisms behind the observed pattern remain elusive However, despite our lack of understanding of the empirical patterns, the democratic peace has become the cornerstone of American foreign policy in the post-Cold War era This increases the urgency and importance of investigating the causal mechanisms that may explain the democratic peace In this paper, we present one approach to doing so, using the tool of agentbased computational simulation We present a computational model of international conflict built on Cederman’s GeoSim model (Cederman, 2003, 2001a; Cederman & Gleditsch, 2002), into which we introduce important roles for domestic structure and for learning Although the analysis in this paper is largely preliminary, early runs of the model produce a number of interesting findings First, basic assumptions about the influence of domestic opposition Domestic structure, learning, and the democratic peace on a state’s ability (or willingness) to initiate conflict suffice to produce a pattern resembling the democratic peace Second, in mixed conflict dyads, democracies are more likely to escalate to war than are autocracies if they are the challenging state which initiated the conflict, but they are less likely to escalate if they are the target state Third, autocracies and democracies alike learn to prefer attacking weaker states Fourth, learning helps reduce the incidence of international conflict as well as slow down the rate of consolidation of states Finally, and surprisingly, introducing a mechanism by which autocracies that attack democracies are punished not only dramatically increases the incidence of international conflict, but also completely eliminates the democratic peace, generating a system in which democracies are noticeably more likely to be at war, regardless of the regime type of their adversary This finding should give pause to those who advocate trying to create a democratic peace through preventative war against autocracies: doing so may not simply be ineffective; it may indeed erode the existing democratic peace Explaining the democratic peace Rousseau’s extensive empirical study (forthcoming) reveals a complex causal process linking domestic politics to international behavior, and finds both monadic and dyadic causal factors informing the democratic peace Specifically, Rousseau shows that democratic states are constrained at the initiation phase by the presence of domestic political opposition that can punish a chief executive for using military force However, Domestic structure, learning, and the democratic peace the use of force by an international opponent reduces domestic opposition to the escalation of conflict once democracies are engaged in militarized crises The chief exception to this process is the dyadic democratic peace: even when they enter a rare crisis, democracies are less likely to escalate against other democracies The importance of domestic opposition emerges in different forms also from other recent work on the democratic peace (Fearon, 1994; Bueno de Mesquita, Smith, Siverson, & Morrow, 2003; Gelpi & Griesdorf, 2001) It remains somewhat unclear, however, whether domestic opposition factors by themselves are sufficient to create a democratic peace such as we find it in the empirical data One possibility is that they suffice to sustain a democratic peace but may not generate one by themselves This raises the issue of the origins of the democratic peace Cederman (2001a) has shown that the democratic peace may have evolved along the lines originally suggested by Immanuel Kant, by states learning to cooperate peacefully Interestingly, however, he finds that such learning is not limited to purely democratic dyads: mixed and purely autocratic dyads also appear to learn to cooperate more peacefully The present paper investigates these issues systematically by testing the implications of different causal mechanisms for the evolution of a democratic peace In particular, we examine (1) the empirical patterns that result from introducing recent theoretical insights about the role domestic opposition plays in democracies as well as autocracies; (2) the impact of different rates of learning from neighboring states on empirical outcomes regarding international conflict; and (3) the possible contribution of a strategy of aggressively punishing autocracies that violate peaceful coexistence to the Domestic structure, learning, and the democratic peace creation of a democratic peace Modeling War and Peace: DomGeoSim A recurring problem in the democratic peace literature is the limited number of cases available to us We have only one ‘run’ for our world, making it very difficult to test the myriad counterfactuals that arise when theorizing different causes for the democratic peace One way around this restriction is to generate additional ‘runs’, by studying the evolution of an artificial world in which states interact, fight, and conquer We apply this approach to the study of the democratic peace, by building an agent-based model of interstate conflict which we can run as often as necessary, while subjecting it to fine-grained changes in the parameters that govern its world Additional ‘world histories’ thus generated cannot, of course, tell us anything about how the real world works, but they can tell us a lot about the validity of our theories for explaining real world patterns For example, if a theory posits a certain causal mechanism as the driver behind an empirical pattern, we can program a simulation in which we can vary that causal mechanism, keeping all other aspects of the world constant If the output remains the same nevertheless — and, importantly, if the other components of the model correctly incorporate any additional assumptions or specifications of the theory — this casts Domestic structure, learning, and the democratic peace serious doubt on the causal mechanism in question As with all methods of investigation, computer simulations have strengths and weaknesses.1 On the positive side of the ledger, five strengths stand out First, as with formal mathematical models, simulations compel the researcher to be very explicit about assumptions and decision rules Second, simulations allow us to explore extremely complex systems that often have no analytical solution Third, simulations resemble controlled experiments in that the researcher can precisely vary a single independent variable (or isolate a particular interaction between two or more variables) Fourth, as suggested above, while other methods of inquiry primarily focus on outcomes (e.g., democratic dyads engage in war?), simulations allow us to explore the processes underlying the broader causal claim (e.g., how does joint democracy decrease the likelihood of war?) Fifth, simulations provide a nice balance between induction and deduction While the developer must construct a logically consistent model based on theory and history, the output of the model is explored inductively by assessing the impact of varying assumptions and decision rules On the negative side of the ledger, two important weaknesses stand out First, simulations have been criticized because they often employ arbitrary assumptions and decision rules (Johnson 1999, 1512) In part, this situation stems from the need to explicitly operationalize each assumption and decision rule However, it is also due to the reluctance of many simulation modelers to empirically test assumptions using alternative methods of inquiry In our model, we address this problem by using assumptions and For a more extensive discussion of strengths and weakness of agent-based modeling, see (Axtell, 2000; Johnson, 1999; Rousseau, 2004) Domestic structure, learning, and the democratic peace interaction rules based on the empirical findings in Rousseau (forthcoming) Second, critics often question the external validity of computer simulations While one of the strengths of the method is its internal consistency, it is often unclear if the simulation captures enough of the external world to allow us to generalize from the artificial system we have created to the real world we inhabit However, this shortcoming is hardly limited to agent-based modeling: all models, even the most thickly descriptive ones, abstract from the real world The more relevant question is whether the elements essential to a particular theory have been incorporated As often as not, criticism that a model is missing some crucial feature indicates that the theory it attempts to test has been incompletely specified Our model builds on Lars-Erik Cederman’s GeoSim model (Cederman, 2003), whose code he generously made available to us Like his, our model is programmed in Java, using the Repast simulation toolkit (see http://repast.sourceforge.net) Although the internal workings of the model have been restructured to allow us, among others, to introduce domestic political structure and learning process, much of the set-up remains the same Cederman has used his model to explore many different aspects of interstate conflict (e.g Cederman, 1997) Indeed, he has previously used it to investigate the democratic peace (Cederman & Gleditsch, 2002; Cederman, 2001b) Examining the implications of strategic tagging, regime influenced alliance formation, and collective security for the emergence of a peaceful liberal world, he found that these three causal mechanisms, first proposed by Kant over two centuries ago, could collectively increase the probability of the emergence of a liberal world Domestic structure, learning, and the democratic peace While Cederman’s innovative research makes an important contribution to the literature, for our purposes it has certain important limitations For example, while Cederman’s model of the democratic peace illustrates conditions under which a stable democratic peace can emerge, he assumes that the dyadic democratic peace exists (Cederman, 2001b: 480) In his simulation, democratic states cannot attack other democratic states by definition In contrast, in our model democracies can (and do) fight each other; the question explored is whether over time democracies might learn to stop fighting each other In order to maximize the flexibility of our adaptation of GeoSim, we have reprogrammed the internal structure of the model, so that configurations other than a straightforward rectangular grid can be modeled (Cederman himself is moving in this direction too) In addition, we have turned many features of the model that were hardwired in the original code into parameters that can be changed by the user The obvious risk here is that the number of parameters can become bewildering to the user On the other hand, however, it dramatically increases our ability to perform robustness checks by testing how dependent our findings are on different, apparently unrelated, parameters To help keep the parameters manageable, we have produced a parameter dictionary, which is attached as an appendix In order to reflect its close relationship to GeoSim, we will refer to our model below as DomGeoSim.2 For a detailed description of GeoSim, see (Cederman, 2003, 2001b) Our model was programmed by Maurits van der Veen While DomGeoSim can produce results very similar to those of GeoSim with the appropriate parameter settings, the results will not be identical, due to the correction of a few minor problems which not alter Cederman’s substantive findings We would like to thank Lars-Erik Cederman for generously providing the original code to us Domestic structure, learning, and the democratic peace Conflict in the DomGeoSim world The model world consists of a population of state agents that interact on a square 50x50 lattice which does not wrap around Each state agent is composed of one or more of the 2500 territory squares, and possesses certain attributes that are modified through interaction with other agents in the landscape In particular, each state has a certain wealth, a domestic structure (autocratic vs democratic, domestic opposition levels), and a set of behavioral rules The individual territory squares are considered ‘provinces’ and international conflict centers around disputes over these provinces Thus, the model is in reality a network in which provinces are connected to a state capital and states that have adjoining provinces can interact with one another The initial number of states is a model parameter, and was set to 100 for all simulations reported here There are three types of states: autocracies, democracies, and pariah states Pariah states are autocracies that have initiated a dispute with a democracy Pariah status wears off over time, but while it lasts it has implications for the likelihood that a pariah state will become enmeshed in a conflict In other words, pariah states behave identically to autocracies, but democracies may behave differently towards them An early ‘state of the world’ snapshot is shown in figure Democracies at peace are light blue and autocracies at peace are light yellow When they go to war, their color becomes darker Pariah states are orange, and become darker red when they go to war Two contiguous states at war have a bright red border drawn between them Normal (notat-war) borders are drawn in black Domestic structure, learning, and the democratic peace 35 Democratization International Studies Quarterly, 44(1), 1-29 Gowa, J (1999) Ballots and Bullets: The Elusive Democratic Peace Princeton, NJ: Princeton University Press Johnson, P E (1999) Simulation Modeling in Political Science American Behavioral Scientist, 42(10), 1509-1530 Kinsella, D., & Russett, B (2002) Conflict Emergence and Escalation in Interactive Internaitonal Dyads Journal of Politics, 64(4), 1045-1068 Lake, D (1992) Powerful Pacifists: Democratic States and War American Political Science Review, 86(1), 24-38 Levy, J S (1989) Domestic Politics and War In R Rotberg & T Rabb (Eds.), The Origin and Prevention of Major Wars (pp 79-100) New York: Cambridge University Press Maoz, Z., & Russett, B (1993) Normative and Structural Causes of Democratic Peace, 1946-1986 American Political Science Review, 87(3), 624-638 Raymond, G A (1994) Democracies, Disputes, and Third-Party Intermediaries Journal of Conflict Resolution, 38(1), 24-42 Reed, W (2000) A Unified Statistical Model of Conflict Onset and Escalation American Journal of Political Science, 44(1), 84-93 Rousseau, D L (2004) Identifying Threats and Threatening Identities: Constructivism in International Relations.Unpublished manuscript, Philadelphia, PA Rousseau, D L (forthcoming) Democracy and War: Institutions, Norms, and the Evolution of International Conflict Stanford, CA: Stanford University Press Stein, A A (1980) The Nation at War Baltimore, MD: Johns Hopkins Press Waltz, K N (1979) Theory of international politics Reading: Addison-Wesley Domestic structure, learning, and the democratic peace 36 Figure Snapshot of the DomGeoSim world Domestic structure, learning, and the democratic peace 37 Figure The international conflict game tree Domestic structure, learning, and the democratic peace 38 Nr Phase Peace Name Power Dispute Power Crisis Power Peace Alliance Dispute Alliance Crisis Alliance Peace Regime type Dispute Regime type Crisis Regime type n.a Regime type 10 n.a Satisfaction Value 1 1 1 2 1 Table Trait Structure of the Model Description Ignore power ratio Only escalate against weaker states Ignore power ratio Only escalate against weaker states Ignore power ratio Only escalate against weaker states Ignore alliance status Only escalate against non-allies Ignore alliance status Only escalate against non-allies Ignore alliance status Only escalate against non-allies Ignore regime type Only escalate against autocracies Only escalate against democracies Ignore regime type Only escalate against autocracies Only escalate against democracies Ignore regime type Only escalate against autocracies Only escalate against democracies Autocracy Democracy Revisionist Status quo Domestic structure, learning, and the democratic peace 39 Trait Regime Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy Democracy Autocracy 43.84 44.05 45.76 46.34 45.49 43.96 48.90 49.66 49.03 49.71 48.33 48.90 30.83 32.42 32.69 31.71 29.69 30.48 55.33 55.09 53.41 52.79 53.74 55.15 50.30 49.44 50.16 49.44 50.88 50.26 33.99 31.37 33.98 31.81 34.00 32.39 34.08 34.97 32.20 35.16 35.14 35.81 T-test 0 0.002 0.001 0 0.56 0.91 0.61 0.88 0.20 0.47 0.08 0.03 0.45 0.04 0.006 0.006 Table Learning: strategy choices for various traits at end of run, by regime type T-test values shown for traits 6-8 are for the difference between the most and least popular trait values (Note: percentages not add to 100 due to small changes in number of states in the system that occur between the counting of different strategies and of the number of states.) Domestic structure, learning, and the democratic peace 40 Variable Fast Medium Nr states at end 59.57 57.34 Fraction of democracies at end 0.38 0.37 Democratic revisionism at end 12.58 14.15 Autocratic revisionism at end 13.59 14.92 Disputes / dyads 6.57 6.66 Crises / dyads 2.39 2.52 Wars / dyads 0.99 1.07 Democratic initiation 1.01 1.03 Autocratic initiation 1.06 1.11 DD crises / DD dyads 2.28 2.38 DD wars / DD dyads 0.86 0.96 Table Implications of the rate of adaptation Slow 48.36 0.41 19.20 18.66 7.35 3.08 1.45 1.20 1.32 2.68 1.17 Variable No pariahs Pariahs Nr states at end 57.34 34.24 Fraction of democracies at end 0.37 0.40 Democratic revisionism at end 14.15 13.68 Autocratic revisionism at end 14.92 15.73 Disputes / dyads 6.66 7.58 Crises / dyads 2.52 4.09 Wars / dyads 1.07 2.69 Democratic initiation 1.03 2.57 Autocratic initiation 1.11 1.21 DD crises / DD dyads 2.38 3.91 DD wars / DD dyads 0.96 3.13 Table Implications of introducing pariah states Domestic structure, learning, and the democratic peace 41 Appendix: DomGeoSim Parameter Dictionary Simulation Set-up Name WorldXSize Description Horizontal dimension of the world grid Default 50 Cederman 50 50 50 Whether the world wraps around left-to-right Type Integer (1…100) Integer, (1… 100) true/false WorldYSize Vertical dimension of the world grid WrapHorizontal false WrapVertical Whether the world wraps around top-to-bottom true/false false MaxRounds StartStationary Number of rounds (steps, ticks) to run the system Round in which to start monitoring system for output, and also possibly to restructure the system by turning some states into democracies Whether to run an approximation of Lars-Erik Cederman’s Geosim2 model Whether to keep track of wars and their size (warCounting in Geosim2) Whether to keep track of governance type (democracies vs autocracies) (democracy in Geosim2) Integer (1…) Integer, less than MaxRounds true/false 5000 20 false (hardcoded) false (hardcoded) 10500 500 true/false true n.a (but true) True true/false true False Cederman CountWars DemocracyMatter s false Initialization Specs Name InitSystem InitPolarity P_hegemon P_revisionist S_neighborhoodType Description Whether to reduce the number of states in the system at the start (if not, every grid location is a state at the start) Number of states desired at the start Probability that a state will receive 10 times the standard quantity of resources at initialization time (Note that this may not matter much if InitSystem is true, since the amalgamation of states will make resource disparities at the individual-territory level rather less noticeable) The probability of becoming a revisionist state at startup The connectivity structure of the world: • – von Neumann neighborhood (only the straight-line neighbors) • – hexagonal neighborhood (6 of the possible neighbors, in an alternating pattern from row to row, to mimic Type true/false Default true Cederman True Integer, (1… WorldXSize* WorldYSize) Fraction (0…1) 50 200 0.2 0.2 Fraction (0…1) 0.2 n.a 0, 1, or 0 Domestic structure, learning, and the democratic peace 42 F_democracies InitDemsAtStart InitDemocracyBias a hexagonal structure) • – Moore neighborhood (all neighbors, including diagonal ones) Fraction of states to turn into democracies at the start (propDem in Geosim2) Whether to turn states into democracies at the start, or (if set to false) at the start of the stationary period) Whether to make democracies stronger at the start If set to true, pick half of the democracies at random from among the 5% most powerful (richest in resources) states, and the other half at random from among the other 95% of states Will go wrong if F_democracies exceeds 10% Fraction (0…1) 0.5 0.1 true/false true False true/false false False Agent Specs Name S_updateRule P_updateType P_changeType Description How to learn from neighboring states: – unless richer than all neighbors, learn from richest neighbor – unless richer than average neighbor, learn from a neighbor whose wealth is above average – if poorer than all neighbors, learn from a randomlyselected neighbor Probability of an attempt to learn from neigbhouring states Probability of a random change in strategy (i.e exploration / mutation) Type 0, 1, or Default Cederman n.a Fraction (0… 1) Fraction (0… 1) 0.1 n.a 0.001 n.a Type true/false Default True Cederman false Fraction (0… 1) Fraction (0… 1) Fraction (0… 1) Positive real (0…) 0.025 1.0 0.0025 n.a 0.005 n.a 10.0 100.0 at start, 1.0 thereafter Resource Settings Name CumulativeResources TaxRate M_growth SD_growth M_harvest Description Whether resource gathering is cumulative from round to round Variable used only for Cederman’s model (WarAndPeace model is always cumulative; Geosim uses the complementary parameter terrRes, true if resources non-cumulative) Fraction of a territory’s resources a capital can extract Mean growth rate of a territory’s resources, per round Standard deviation of the growth rate Mean harvest size for set-ups with cumulative resources (Geosim uses mRes and mHarvest here) Domestic structure, learning, and the democratic peace 43 SD_harvest Standard deviation of the harvest size (Geosim uses sRes and sHarvest here) Positive real (0…) 5.0 ConsumeFixed 50.0 at start, 5.0 thereafter true Whether to consume a fixed share of resources True/false False each round Consumption Fraction of resources fixed from one round to the Fraction (0… 0.0022 0.99 next Used only for Cederman’s model when 1) resources non-cumulative (Geosim uses a complementary parameter, called resChange, representing the fraction that changes from one round to the next) ProDemocracyBias Resource extraction bias modifier for Positive real 1.0 1.0 democracies A states’ total extraction of (0…) resources is multiplied by this value (i.e a value below means democracies extract fewer resources; above means they extract more than autocracies) Used only if Cederman is true (demBias in Geosim2) Note: double-check that ConsumeFixed and Consumption are described correctly More generally, need to double check once more all the resource functions, since war chests end up being very small, while average GDP per province gradually increases Distance Settings Name S_distanceCosts DistanceGradient T_distance SD_distance Description The way in which a state’s ability to extract resources from distant provinces as well as its ability to mobilize forces in those provinces to face opponents there are affected by distance (comparable parameter in Geosim2 is distRes) • – no costs associated with distance • – costs follow a geometric pattern (distance^gradient) With the default settings, the fraction of resources extractable/mobilizable at successive integer distances is: – 1, – 0.5, – 0.33, – 0.25, – 0.2, – 0.17 • – costs (apart from offset) follow a logistic pattern: 1/(1+e^(slope*ln(distance/threshold))) With the default settings, the fraction of resources extractable/mobilizable at successive integer distances is: – 0.90, – 0.55, – 0.31, – 0.2, – 0.15, – 0.13, – 0.12 Used when S_distanceCosts = Extractive and mobilizing ability are calculated as (distance from province to capital)^DistanceGradient So for the default value of -1, this is 1/(distance from province to capital) Threshold of distance at which the inflection point of the logistic curve occurs (distDrop in Geosim2) Standard deviation of the initial distance threshold, relevant for distance-loss functions that are state- Type 0, 1, or Default Cederman Positive real (0…) -1.0 0.0 Positive real (0…) Positive real (0…) 2.0 2.0 0.0 0.0 Domestic structure, learning, and the democratic peace 44 DistanceSlope DistanceOffset specific, i.e as a result of shocks (distDropSD in Geosim2) Slope of logistic distance-loss curve The fraction of resources unaffected by the logistic distance loss function Positive real (0…) Fraction (0… 1) 3.0 3.0 0.1 0.1 Type Fraction (0…1) Fraction (0…1) Default 0.05 Cederman 0.01 0 Fraction (0…1) 0.25 n.a true/false true true Fraction (0…1) Fraction (0…1) 0.2 0.2 0 true/false true true Fraction (0…1) 0.1 0.1 Type Fraction (0… 1) Default 0.5 Cederman 0.5 Fraction (0… 1) true/false 0.33 0.1 true true true/false false true Conflict Initiation Name P_unprovokedAttack Description The probability of initiating an unprovoked conflict P_selectWeakest The probability, when initiating a conflict, of selecting the weakest possible target, as opposed to a randomly-selected possible target The probability that a revisionist state will behave opportunistically and attack a neighboring state already embroiled in a conflict Whether to allow campaigns If true, states will keep fighting against opponents even after a territory changes hands The ‘unit’ of a war is a singleterritory conflict If true, states will go on attacking the target of their campaign as long as they share a border (governed, of course by the value of P_dropCampaign) The probability of dropping a campaign against a state The probability a state will initiate another conflict (i.e without provocation) if already involved in a conflict Whether to go on the alert when a neighboring state is currently embroiled in a conflict (activeNeighs in Geosim2) Once on the alert (for example because a neighbor is in a conflict), the probability that alert status will be dropped (dropActive in Geosim2) P_revOpportunism Campaigns P_dropCampaign P_twoFrontWar MonitorNeighbors P_dropAlert Conflict Implementation Name F_mobile F_warCost NoisyWar BalanceFront Description The fraction of resources in a state’s war chest that is mobile and can be allocated according to the strength of opposing forces The remaining fraction is evenly divided across all fronts (mobil in Geosim2) Cost of a war to one’s opponent, expressed as a fraction of the resources one has mobilized at the front Whether the outcome of a battle (win/loss) is deterministic or stochastic (in Geosim2, not a separate parameter, but true unless VictorySlope = 0) Whether to consider only the resources a state has Domestic structure, learning, and the democratic peace 45 SuperiorityRatio SuperioritySlope SD_superiority VictoryRatio VictorySlope mobilized at our mutual front, or instead against the sum total of the resources it has in its war chest (not a variable in Geosim2 — always balance against front only; in WarAndPeace balancing against a front is not meaningful since a front only exists if a war is being fought) The superiority ratio used to decide whether to attack an opponent (sup in Geosim2) Depending on the presence of alliances and the value of parameter BalanceFront, if the relevant resources of a state are X times greater than those that can be mustered by the target, a state will consider the target attackable The slope of the logistic function used to decide whether to attack an opponent (if 0, then simply use the SuperiorityRatio value) (supC in Geosim2) The logistic function is analogous to that for distance loss, apart from the offset: 1/ (1+e^(slope*ln(SuperiorityRatio/actualPowerRatio))) With the default values, this means that the probability a state will find a target attackable, for various power ratios is: – 3E-10, – 0.0003, – 0.5, – 0.997, – 0.99997 Standard deviation of the superiority ratio, relevant for ratios (and thus attack decisions) that are state-specific, i.e as a result of shocks (supSD in Geosim2) Functions analogously to SuperiorityRatio above, but now for deciding whether a state will win (vict in Geosim2) When shocks apply, and thus state-specific ratios, a state’s victory ratio is always kept equal to its superiority ratio Functions analogously to SuperioritySlope above, but now for deciding whether a state will win (victC in Geosim2) Positive real (0…) 3.0 3.0 Positive real (0…) 20.0 20.0 Positive real (0…) 0 Positive real (0…) 3.0 3.0 Positive real (0…) 20.0 20.0 Type Fraction (0… 1) Integer Default 0.2 Cederman 100 n.a Integer 20 true/false False true true/false true true Conflict Resolution Name P_peace Patience MaxWarMemory DisintegrateCutoff s KeepConnected Description The probability that a given war will end suddenly in any given round, with a peace treaty The number of rounds a state will allow its opponent in a dispute or crisis simply to delay taking further action The number of rounds after a war is over that a state will remember it was in a war (called maxShadow in Geosim2) When a capital is conquered, or when a section of a state is cut off from the rest, whether to keep cut-off or head-less provinces together as one or more new states, or instead to atomize them all into singleprovince states Whether to keep all territories in a state contiguous Domestic structure, learning, and the democratic peace 46 Alliance Settings Name AllowAlliances T_invokeAllianc e P_aidAllies AllyContribution Description Whether to allow alliances The resource ratio beyond which one is willing to join an alliance against an opposing state For example, if 5, then we join an alliance against an opponent if that opponent has times as many resources as we in our war chest or at our mutual front (depending on the value of BalanceFront) (in Geosim2, this parameter is called minThreat and is negative, but otherwise with the same implications) The probability that one will come to the aid of one’s allies (i.e defect against an alliance target) (prOblig in Geosim2) The proportion of the forces of states allied with a target that a state takes into account when deciding whether or not to attack that target (contrib in Geosim2) Type true/false Integer Default true Cederman false 2.8 Fraction (0… 1) 0.5 0.5 Fraction (0… 1) 0.5 0.5 Type true/false Default true Cederman false Fraction (0…1) 0.8 n.a Fraction (0…1) 0.002 0.002 10 Democracy Settings Name Democratize T_regimeChange P_democratize Description Whether states are subject to coups and democratizations (democratization in Geosim2) The threshold in terms of wealth above beyond which a state may change regime For a value of 0.8, if wealth per territory (a proxy for GDP per capita) is in the top 20% of states and a state is an autocracy, it may turn into a democracy, whereas if wealth per territory is in the bottom 20%, a democracy may turn into an autocracy, as determined by P_democratize and P_coup, respectively The probability that an autocracy will turn into a democracy in any given round In Geosim2, this probability is modified by a complicated ad hoc function involving a logistic calculation based on the degree to which a state is surrounded by democracies The parameter value is multiplied by the resulting value, which, for different fractions of surrounding democracy, is: 0.1 – 0.56, 0.2 – 0.62, 0.5 – 0.77, 0.9 – 0.89, 1.0 – 0.91 In WarAndPeace, the probability only comes into effect if the regime change wealth threshold (T_regimeChange) is met Moreover, in WarAndPeace, regime change may also take place based on domestic opposition, instability, or random exploration (see P_changeType above) Domestic structure, learning, and the democratic peace 47 P_coup The probability that a democracy will turn into an autocracy in any given round In Geosim2, modified in the same way as P_democratize, except now we count the fraction of surrounding states that are already autocracies Comments for WarAndPeace are the same as under P_democratize P_losePariahStatus The probability that a state marked as a pariah will lose its pariah status If 0, states will never be marked as pariahs (collSec in Geosim2) P_targetPariah The probability that a pariah state will be targeted for an unprovoked escalation by a democracy it borders Double-check implicit value of P_targetPariah in GeoSim Fraction (0…1) 0.001 0.001 Fraction (0…1) 0.25 Fraction (0…1) 0.25 (?) Domestic structure, learning, and the democratic peace 48 11 Domestic Opposition Name F_domOpp_DemMin Description Type Default Min level of domestic opposition for a new Fraction 0.2 democratic state (0…1) F_domOpp_DemMax Max level of domestic opposition for a new Fraction 0.6 democratic state (0…1) F_domOpp_AutMin Min level of domestic opposition for a new Fraction 0.2 autocratic state (0…1) F_domOpp_AutMax Max level of domestic opposition for a new Fraction 0.6 autocratic state (0…1) SD_opposition Standard deviation of the normal function from Fraction 0.05 which the next round’s initial opposition level is (0…1) drawn (with mean equal to the current round’s opposition level) RepressOpposition Reduction in the opposition level that autocracies Fraction 0.02 can achieve in a given round (will be modified by (0…1) their length of tenure as an autocracy), expressed as a fraction of the maximum possible reduction RallyMin Minimum level by which domestic opposition will Fraction fall if a state is attacked (expressed as a fraction of (0…1) the maximum possible drop) RallyMax Maximum level by which domestic opposition will Fraction 0.05 fall if a state is attacked (0…1) Note: Currently rallying takes place every time a conflict shifts into a higher phase, and both attacker and attacked have the rally effect Cederman n.a n.a n.a n.a n.a n.a n.a n.a 12 Shock Settings Name P_shock TaxShock TechnoShock ProDemBias_Shock s Description Probability that a state will be subjected to a shock in any given round, starting from StartStationary (see above) Size of the extractive shock The shock affects the distance threshold (i.e the inflection point of the logistic distance loss function) A shocked state’s distance threshold is set to the default model threshold plus the fraction of the shock size that corresponds to the fraction of the period between StartStationary and MaxRounds that has elapsed In other words, the threshold will gradually increase over time, meaning extraction ability improves over time If true, a shocked state’s victory and superiority ratios will be reset to the result of a random draw from a normal distribution with mean SuperiorityRatio and standard deviation SuperiorityRatio*SD_superiority Degree to which democracies are more or less likely to be shocked Multiplied by the shock Type Fraction (0… 1) Default 0.0001 Cederman 0.0001 Real 20.0 20.0 true/false false false Positive real (0…) 1.0 1.0 Domestic structure, learning, and the democratic peace 49 probability, so that a value below means democracies are less likely to be shocked, and a value above means they are more likely to be 13 Output Choices Name S_WarSizeMeasure ReportInterval Description The measure used to gauge the size of wars: – the cost of the war – the duration of the war, in rounds – the total number of states involved in the war – the duration * the total number of states – the number of state-rounds (less than 3, since not every state will be involved in every round the war is ongoing) All of these values are written to the output, but only the selected one is displayed, if the war-size chart is displayed during the run The interval at which data gets written to a file Data about wars is written out for each war, but tracking data about the number of states, ongoing conflicts, etc is only written out every ReportInterval rounds (as well as prior to round 1) Type 0, 1, 2, 3, or Default Cederman Integer 20 n.a .. .Domestic structure and the democratic peace Domestic structure, learning, and the democratic peace DOMESTIC STRUCTURE, LEARNING, AND THE DEMOCRATIC PEACE: AN AGENT-BASED COMPUTATIONAL SIMULATION. .. Snapshot of the DomGeoSim world Domestic structure, learning, and the democratic peace 37 Figure The international conflict game tree Domestic structure, learning, and the democratic peace 38... studies, and computer simulations Many authors prefer to restrict the use of the term “gene” to situations involving death and reproduction Domestic structure, learning, and the democratic peace