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Terrorist Innovations and Anti-TerroristPoliciesJoão Ricardo Faria aa Department of Economics and Finance , University of Texas PanAmerican , Edinburg, TX, USAPublished online: 25 Jan 2007.
To cite this article: João Ricardo Faria (2006) Terrorist Innovations and Anti-Terrorist Policies,Terrorism and Political Violence, 18:1, 47-56, DOI: 10.1080/095465591009377
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Terrorist Innovations and Anti-Terrorist Policies
JOAO RICARDO FARIA
Department of Economics and Finance, University ofTexas Pan American, Edinburg, TX, USA
This paper examines a dynamic model of terrorist attacks and innovations. Terroristinnovations are triggered by counterterrorist policies. The model allows us tocompare the effectiveness of three different anti-terrorist policies: deterrence, pre-emption and intelligence. The paper argues that intelligence is the most effectiveanti-terrorist policy and deterrence is the least effective. However, this conclusionmay change when one considers the possible positive effects of intelligence onterrorist innovations or when intelligence is gathered to be used for preemptionand deterrence.
Introduction
Anti-terrorist policies can be defensive (deterrence) or proactive (preemption).1
Defensive policies create obstacles for terrorist action aimed at reducing the prob-ability of success of terrorist attacks2 and include the creation of technology-basedbarriers, instituting of stricter laws and penalties, and hardening of targets. Proactivepolicies aim at preventing attacks by disabling terrorists and include group infil-tration, preemptive strikes, and retaliatory raids or invasion of state sponsors.3 Asany terrorist organization has objectives that they try to achieve using limitedresources, one can think of anti-terrorist policies by examining how they affect theobjectives and constraints of terrorist organizations.
For instance, defensive policies such as the use of metal detectors and sky mar-shals may reduce skyjackings because it makes it harder for terrorists to embark onand use arms in commercial flights. However, despite the efficiency and success ofthis particular policy, this is not the end of the story. Terrorist organizations canchoose their tactics and activities. The introduction of metal detectors in airportsincreases the price for terrorists to engage in skyjackings and this price increase,other things constant, generates substitution and income effects.4 Assuming a verysmall income effect, and focusing on the substitution effect, we must expect that thisparticular anti-terrorist policy creates incentives for terrorists to diversify by engag-ing in attacks other than skyjackings.
I would like to thank, without implicating, one anonymous referee, Daniel Arce, AdolfoSachsida, Todd Sandler, Kevin Siqueira, and conference participants of ‘‘The PoliticalEconomy of International Terrorism,’’ Center for International Studies (CIS), University ofSouthern California.
Joao Ricardo Faria [Ph. D. Economics, University of Kent at Canterbury, 1998] is anAssociate Professor of Economics, University of Texas Pan American.
Address correspondence to J. R. Faria, University of Texas Pan American, Departmentof Economics and Finance, College of Business Administration (COBA), 1201 W. UniversityDrive, Edinburg, TX 78539-2999. E-mail: [email protected]
Terrorism and Political Violence, 18:47–56, 2006Copyright � Taylor & Francis Group, LLCISSN: 0954-6553 print=1556-1836 onlineDOI: 10.1080/095465591009377
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Initially, the economic literature on terrorism,5 pioneered by William Landes,6
studied the effectiveness of anti-terrorist policies ignoring the substitution effect.The influential paper of Walter Enders and Todd Sandler7 changed the literatureby showing that policies designed to reduce one type of attack affect otherattack modes. In the specific case of metal detectors in airports, Enders and Sandlershowed that they reduce skyjackings but increase other kinds of hostage attacks andassassinations.8
Another kind of substitution effect involves an intertemporal allocation ofresources.9 The intertemporal substitution of resources allows terrorists to decidewhether to attack in the present or in the future. For instance, the retaliatory raidsby Israel against Palestinian terrorists (PLO) in 1972 initially increased the numberof PLO attacks against Israel.10 Similarly, the U.S. retaliatory raid against Libya in1986 increased terrorist attacks against U.S. interests.11 However, in both cases, thehigh level of attacks had very little persistence and due to the exhaustion of terroristresources, the mean number of attacks directed against Israel and the U.S. remainedunchanged. This suggests that retaliatory raids induce terrorists to intertemporallysubstitute attacks planned for the future into the present.
Overall, an important empirical regularity arises from the substitution effect. Asterrorists substitute between attack modes, or substitute intertemporally, the timeseries of all terrorist incidents are characterized by cycles.12
The findings that substitution effects are important have policy implications. Ifgovernments aim at curbing terrorism, they must not focus on just one type of event,instead they must target simultaneously a wide range of terrorist attack modes. Orequivalently, governments must go after terrorist resource endowments such as theirfinances, leadership, and membership.13 This implies that governments must use notonly deterrence but also preemption.14
Additionally, governments must try to anticipate and to guard against terroristsubstitution. According to the view expressed above, whenever a government recog-nizes how terrorists have been acting and address it through deterrence and preemp-tion policies, terrorists change their action. Beyond the substitution effects seenabove, there is another way terrorists change their organization, strategy, andattacks in response to government anti-terrorist policies: terrorists innovate.15 Byinnovation we mean that terrorists do what they have never done before, i.e., theycreate new modes of attack, they use new techniques and weapons, they choosenew targets, they change their methods of recruitment and organization, etc. Asexamples of innovations in modes of attack we can cite the use of commercial air-planes to attack buildings as in the September 11 attacks or to attach explosivesto shoes as in the case of the shoe-bomber Robert Reid. Therefore, preemptionand deterrence policies not only trigger the substitution effects but also the ‘‘inno-vation effect.’’16
One important implication of the innovation effect is that they make govern-ments always lag behind trying to catch up with terrorists. In order to stop this race,governments have to find experts who can advise on what the next terrorist inno-vation is. This is where intelligence plays an important role. Intelligence is definedhere as the anti-terrorist policy designed to tackle the innovation effect. Therefore,governments have to invest not only in defensive and proactive counterterrorist mea-sures, but also in intelligence in order to face terrorist innovations.
The comparison of anti-terrorist policies is crucial to allocate governmentresources in the fight against terrorism. Daniel Arce and Todd Sandler17 tackle this
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issue in a game-theoretic framework and show that deterrence is prevalent overpreemption when targeted governments can choose between either policies or employboth.18 The predisposition towards defensive counterterrorist measures results in anequilibrium with socially inferior payoffs when contrasted with proactive responses.19
The objective of this paper is to provide a simple model of terrorist innovationsin which we can compare the efficiency of anti-terrorist policies. However, it uses adifferent framework from that of Arce and Sandler to evaluate counterterrorist poli-cies. Our model is based on a dynamic system that focuses on the innovation effectand addresses its impact on the efficiency of three different anti-terrorist policies:deterrence, preemption, and intelligence. It is shown that under simple conditionsan order among anti-terrorist policies emerges, where intelligence is the best policyand deterrence is the worst policy. This order may change when issues such as theinteraction of anti-terrorist policies and the possible positive effect of intelligenceon terrorist innovations are taken into account.
The Model
In what follows I attempt to assess the effectiveness of anti-terrorist policies in a verysimple dynamic model. The idea is to rank the anti-terrorist policies of deterrence,preemption, and intelligence, so as to decide which one deserves a larger share ofthe government budget.
According to the discussion in the introduction, the rate of growth of terroristattacks ( bAA � _AA=A, where _AA � dA=dt) is an increasing function of terrorist innova-tions (T). That is, whenever terrorists innovate, this introduces an element of sur-prise, which makes their actions more effective. For a given level of terroristtechnology the government employs deterrence (E) and preemption (P) aiming atreducing the growth rate of terrorist attacks. The direct effect of deterrence iscaptured by the parameter b, while preemption is used to reduce the availabilityof terrorist resources20 (r) and consequently affects bAA indirectly. By decreasingterrorist resources, preemption decreases the effectiveness of terrorist technologyon the rate of growth of terrorist attacks.
bAA ¼ rðPÞT � bE ð1Þ
where terrorist resources are negatively related to preemption: dr=dP < 0.The innovation effect is captured by the idea that deterrence policies, such as the
installation of metal detectors in airports, may trigger the development of terroristinnovations. Therefore we assume that the rate of growth of terrorist technologyðbTT � _TT=TÞ increases with deterrence (E). The marginal effect of deterrence on bTT isgiven by c, which is a proxy for the innovation effect. Intelligence (I) aims at antici-pating terrorist innovations, thus, in line with my account, it is designed to tackle theinnovation effect. In this vein, it is assumed that intelligence negatively affects theinnovation effect: cðIÞ > 0; dc=dI < 0.
In addition, I assume a negative relationship between bTT and the successful levelof terrorist attacks associated with a given level of technology [qAT]—where theparameter q is the rate of success of terrorist attacks—since terrorists have no incen-tive to innovate as long as their actual methods are successful. The expression forbTT is the following:
bTT ¼ cðIÞE � qAT ð2Þ
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It is possible to study how to derive equations (1) and (2) from optimal intertem-poral decisions of terrorists by taking into account the government anti-terroristpolicies as constraints. Faria, for instance, analyzes a differential game between ter-rorists and the government in which terrorists maximize the number of attacks sub-ject to a constraint that combines terrorists’ resources and government anti-terroristpolicies.21 This paper could follow the same procedure; however, it would make mymodel too complex without providing any substantive result regarding anti-terroristpolicies in comparison with the present setup. Consequently, I keep the simpleframework given by the dynamic system (1) and (2) and make inferences regardingthe effectiveness of the government anti-terrorist policies of deterrence (E), preemp-tion (P) and intelligence (I).
In order to tackle this issue, I rewrite equations (1) and (2) as a pair of ordinarydifferential equations:
_AA ¼ A½rðPÞT � bE� ð3Þ
_AA ¼ T ½cðIÞE � qAT � ð4Þ
It is worth noticing that equation (4) is a logistic growth function for terrorist tech-nology. It states that the relative rate of growth of terrorist technology is relatively con-stant while terrorist technological level is small, but that it converges to zero when thetechnological sophistication of terrorists reaches an upper limit.22 That is, when thenumber of successful terrorist attacks qA is zero it implies by equation (4) that_TT ¼ cðIÞET and terrorist innovations (T) increase exponentially. As q becomes larger,the growth-inhibiting factor of terrorist innovations is associated with the actual inten-sity of terror technology in successful terrorist attacks, given by the term qAT2. Intuit-ively this happens because when terrorist attacks have been successful for a given levelof technology, terrorists have fewer incentives to innovate and the relationship between_TT and T is non-linear as described by equation (4): _TT ¼ cðIÞET � qAT2.
One can find the steady-state equilibrium of the system (3)–(4) by making:_AA ¼ _TT ¼ 0. It is easy to verify that the following point is a steady-state equilibrium(also called constant solutions):
A� ¼ rðPÞcðIÞbq
T� ¼ b
rðPÞE:
The properties of the above equilibrium can only be analyzed through the com-parative statics analysis if it is a stable equilibrium. In order to examine the stabilityof this equilibrium I consider the Jacobian (J), defined below, evaluated at the equi-librium point (A�, T�):
J ¼@ _AA
@A
@ _AA
@T@ _TT
@A
@ _TT
@T
2664
3775ðA�;T�Þ
¼ 0 rðPÞA�
�qðT�Þ2 �qA�T�
� �
As the determinant of J is a positive number: det J ¼ q rðPÞA�ðT�Þ2 > 0, and thetrace of J is a negative number: tr J ¼ �qA�T� < 0, it implies that the steady-state
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equilibrium (A�, T�) is stable. One can show that this equilibrium can be a stablenode or a stable focus.23
Figure 1 depicts the steady-state equilibrium as a stable focus. One can see thatfor a given initial value of A and T the system converges to the steady-state equilib-rium A� and T� in a clockwise manner. That is, initially T decreases while A increasesand when A starts to decrease then T begins to increase so that the adjustment over-time, shown in Figure 2, is cyclical and convergent, and as soon as the steady-statepoint [A�, T�] is reached, A and T stop changing and they tend to remain constant.
Having found the steady-state equilibrium and verified its stability I proceedwith the comparative statics analysis in order to assess the effectiveness of the anti-terrorist policies. In this sense attention must be paid to the following multipliersgenerated by the comparative statics analysis:
1)dA�
dE¼ 0;
dT�
dE¼ b
rðPÞ > 0
2)dA�
dP¼ cðIÞ
bq
dr
dP< 0;
dT�
dP¼ � bE
½rðPÞ�2dr
dP> 0
3)dA�
dI¼ rðPÞ
bq
dc
dI< 0;
dT�
dI¼ 0
The inspection of multipliers 1), 2) and 3) allows us to compare the effectivenessof each anti-terrorist policy and it yields the main result of this paper, embodiedin Result 1.
Result 1: Deterrence (E) when compared to preemption (P) and intelligence (I) isthe worst anti-terrorist policy, since by 1) it has no effect on the steady-state value ofterrorist attacks A�, dA�=dE ¼ 0, and it increases the steady-state value of terroristtechnology T�, dT�=dE ¼ b=rðPÞ > 0. It is easy to see that the best anti-terrorist
Figure 1. The steady-state equilibrium as a stable focus.
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policy is the use of intelligence since by 3) it decreases A�, dA�=dI ¼ ðrðPÞ=bqÞðdc=dIÞ, and does not affect T�, dT�=dI ¼ 0. Preemption (P) is better than deterrencebut worse than intelligence, since by 2) on the one hand it decreases A�, dA�=dP ¼ðcðIÞ=bqÞðdr=dPÞ < 0, but on the other hand it increases T�, dT�=dP ¼ �ðbE=½rðPÞ�2Þðdr=dPÞ > 0.
Therefore, Result 1 establishes an order for the anti-terrorist policies, indicatingthat deterrence is the least effective and intelligence is the most effective, while pre-emption lies between them.24 This somewhat surprising result arises because thereis a feedback mechanism between deterrence and terrorist innovations describedby the innovation effect.
The policy recommendation in line with a simple government maximizationproblem is that as anti-terrorist resources are scarce the government should allocatemore resources to intelligence than to preemption and more resources to preemptionthan to deterrence. In practical terms this means that by focusing on one simpledimension of antiterrorist policies, let us say personnel, one can argue that one dollarspent in personnel gathering intelligence is more effective in combatting terrorismthan the same dollar spent in personnel attached to deterrence.25
By focusing at the individual effect of each policy on terrorist attacks and terror-ist technology, one may point out that the overall comparison of the policies as itappears in result 1 does not hold. This raises the issue of individual comparison ofeach anti-terrorist policy on A� and T� which follows from the multipliers aboveand is presented below:
I)dA�
dI¼ rðPÞ
bq
dc
dI<
dA�
dP¼ cðIÞ
bq
dr
dP<
dA�
dE¼ 0
II)dT�
dE¼ b
rðPÞ >dT�
dP¼ � bE
½rðPÞ�2dr
dP>
dT�
dI¼ 0
Figure 2. The adjustment of A and T over time towards equilibrium.
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Condition I) shows that the most effective policy to reduce terrorist attacks, A�,is intelligence. Moreover, it also shows that intelligence is more effective than pre-emption in reducing A�. In order for this last inequality to hold it is necessary that:
1
cðIÞdc
dI<
1
rðPÞdr
dPð5Þ
that is, the intelligence semi-elasticity of the innovation effect ðð1=cðIÞÞðdc=dIÞÞ,must be lower than the preemption semi-elasticity of terrorist resources ðð1=rðPÞÞðdr=dPÞÞ.
Condition II) establishes that the positive impact of deterrence on terrorist tech-nology, T�, is greater than the positive impact of preemption. In other words, it saysthat deterrence is even less effective than preemption in tackling T�. This inequalityholds true if the ratio between terrorist resources and deterrence ðrðPÞ=EÞ is greaterthan the absolute value of the marginal effect of preemption on terrorist resourcesð�dr=dPÞ:
rðPÞE
> � dr
dPð6Þ
Provided that inequalities (5) and (6) hold, we can order each policy individuallyand, again, intelligence is the most effective policy to fight A� and T� while deter-rence is the least effective.
Extensions of the Model
The model presented in the previous section allows for extensions where we canincorporate new issues previously ignored. In this section we address two importantmatters. The first one is the possible positive effects of intelligence on terrorist inno-vations. For instance, efforts in the 1970s and 1980s to infiltrate the fighting commu-nist organizations in Europe led them to adopt a cell structure—this innovation wasso effective that even the CIA adopted it. In order to capture this positive effect ofintelligence on terrorist innovations, let us modify equation (4) accordingly:
_TT ¼ T ½cðIÞUðIÞE � qAT � ð40Þ
where the function U is a positive function of I: dUðIÞ=dI > 0.Taking into account the system formed by equations (3) and (40), we have as
steady-state equilibrium: A�� ¼ ðrðPÞcðIÞ=bqÞUðIÞ and T� ¼ ðb=rðPÞÞE, which is,as before, a stable equilibrium. The comparative statics analysis shows us that:
dA��
dI¼ rðPÞ
bqUðIÞ dc
dIþ cðIÞ dU
dI
� �< 0 , UðIÞ dc
dI< �cðIÞ dU
dI
by contrasting this multiplier with dA�=dI ¼ ðrðPÞ=bqÞðdc=dIÞ < 0, we see that thereis a loss of efficiency in intelligence when there is a positive effect of intelligence onterrorist innovations. Furthermore, the loss of efficiency can be strong enough so asto make intelligence less efficient than deterrence and preemption.
The second issue ignored in the basic model is with regard to the interactionamong the three anti-terrorism policies. Basically, intelligence is gathered to be usedfor preemption and deterrence and there are substitutions and complementarities
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among these three policies. Of course, to fully explore these interactions is beyondthe scope of this paper and demands an entirely new paper on this subject. Thus hereis assumed a very simple type of interaction between intelligence and the other twoanti-terrorist policies. We assume that intelligence improves the efficiency of deter-rence and preemption:
E ¼ EðIÞ; dE
dI> 0
P ¼ PðIÞ; dP
dI> 0
ð7Þ
By considering the dynamic system formed by equations (3) and (4) the steady-state solution is: A� ¼ ðrðPðIÞÞcðIÞÞ=bq and T� ¼ ðb=rðPðIÞÞÞEðIÞ. The introductionof the interaction among policies changes the impact of intelligence (I) on the steady-state equilibrium A� and T�. Intelligence becomes more efficient against terroristattacks and less efficient against terrorist innovations:
dA�
dI¼ rðPðIÞÞ
bq
dc
dIþ cðIÞ
bq
dr
dP
dP
dI< 0;
dT�
dI¼ b
rðPðIÞÞdE
dI� bEðIÞrðPðIÞÞ2
dr
dP
dP
dI> 0
In addition, due to the interactions assumed in (7) I can no longer contrast intel-ligence with deterrence and preemption in terms of efficiency. However, I still cancompare deterrence with preemption. Holding intelligence constant, preemption ismore efficient than deterrence.
Concluding Remarks
This paper examines a simple dynamic system that describes the evolution of terror-ist attacks and terrorist innovations. It is assumed that the rate of growth of terroristattacks increases with terrorist innovations. The government employs deterrence andpreemption to reduce the growth rate of terrorist attacks. However, deterrence trig-gers the development of terrorist innovations. The government uses intelligence toanticipate terrorist innovations aiming at decreasing its growth rate.
The model has one stable steady-state equilibrium in which the equilibrium levelof terrorist attacks decreases with preemption and intelligence and is not affected bydeterrence, while the equilibrium level of terrorist technology increases with preemp-tion, and deterrence and is not affected by intelligence. Therefore the model gener-ates a rank for these anti-terrorist policies. The most effective anti-terrorist policyis intelligence and the least effective is deterrence.
However, this rank is sensitive to modifications in the basic model. By extendingthe basic model to address the positive effects of intelligence on terrorist innovationsand the interaction among the anti-terrorism policies, we show that the rankobtained in the simple model may change. The positive effect of intelligence on ter-rorist innovations decreases the efficiency of intelligence on terrorist attacks. How-ever, if the loss of efficiency is not large enough, then intelligence continues to bemore efficient than deterrence and preemption.
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When the interaction among the anti-terrorism policies is considered, assumingthat intelligence is gathered to help deterrence and preemption, we cannot contrastintelligence with deterrence and preemption. However, in this case as in the baselinemodel, preemption is more efficient than deterrence to fight terrorism.
Notes
1. For a list of anti-terrorist policies, see Walter Enders and Todd Sandler, ‘‘The Effec-tiveness of Antiterrorism Policies: A Vector-Autoregression Intervention Analysis,’’ AmericanPolitical Science Review 87 (1993): 829–44.
2. H. Lapan and T. Sandler, ‘‘To Bargain or Not To Bargain: That Is The Question,’’American Economic Review Papers and Proceedings 78 (1988): 16–21.
3. Deterrent threats are a specific type of proactive antiterrorist policy. For instance,according to James J. Wirtz, ‘‘Antiterrorism via Counterproliferation,’’ (N.A.) http:==atlas.usafa.af.mil=inss=terrchp8.htm, counterproliferation of chemical, biological, and nuclearweapons (in general, weapons of mass destruction—WMD) is effective to combat terrorismsince it allows U.S. forces to retaliate after military units or civilian targets suffer a WMDattack. Therefore, American policymakers can make credible deterrent threats to discouragestate-sponsored terrorism.
4. See Todd Sandler, John T. Tschirhart, and Jon Cauley, ‘‘A Theoretical Analysis ofTransnational Terrorism,’’ American Political Science Review 77 (1983): 36–54.
5. E.g., Jon Cauley and Eric I. Im, ‘‘Intervention Policy Analysis of Skyjacking andOther Terrorist Incidents,’’ American Economic Review Papers and Proceedings 78 (1988):27–31; Walter Enders, Todd Sandler, and Jon Cauley, ‘‘U.N. Conventions, Technology andRetaliation in the Fight Against Terrorism: An Economic Evaluation,’’ Terrorism and Polit-ical Violence 2 (1990): 83–105; and Walter Enders, Todd Sandler, and Jon Cauley, ‘‘Assessingthe Impact of Terrorist-Thwarting Policies: An Intervention Time Series Approach,’’ DefenceEconomics 2 (1990): 1–18.
6. William Landes, ‘‘An Economic Study of U.S. Aircraft Hijackings, 1961–1976,’’Journal of Law and Economics 21 (1978): 1–31.
7. Enders and Sandler, ‘‘Effectiveness of Antiterrorism Policies.’’8. For an overview on the evolution of the economic literature on terrorism, see Walter
Enders and Todd Sandler, ‘‘What Do We Know About the Substitution Effect in Transna-tional Terrorism?’’ (2004) in Researching Terrorism: Trends, Achievements, Failures (forth-coming), eds. A. Silke and G. Ilardi (London: Frank Cass) and Todd Sandler, ‘‘FightingTerrorism: What Economics Can Tell Us,’’ interview by Jeff Madrick, Challenge 45 (2002):5–18. See also Walter Enders and Todd Sandler, ‘‘Terrorism: Theory and Applications,’’ inHandbook of Defense Economics, eds. Keith Hartley and Todd Sandler (Amsterdam: Elsevier,1995), 213–49.
9. An intertemporal allocation of resources is an allocation that occurs during a periodof time.
10. Bryan Brophy-Baermann and John Conybeare, ‘‘Retaliating Against Terrorism:Rational Expectations and the Optimality of Rules Versus Discretion,’’ American Journal ofPolitical Science 38 (1994): 196–210.
11. Enders and Sandler, ‘‘Effectiveness of Antiterrorism Policies.’’12. E.g., Eric I. Im, Jon Cauley, and Todd Sandler, ‘‘Cycles and Substitutions in Terror-
ist Activities: A Spectral Approach,’’ Kyklos 40 (1987): 238–55; Walter Enders, G. F. Parise,and Todd Sandler, ‘‘A Time-Series Analysis of Transnational Terrorism: Trends and Cycles,’’Defence Economics 3 (1992): 305–20; Walter Enders and Todd Sandler, ‘‘Transnational Ter-rorism, 1968–2000: Thresholds, Persistence and Forecasts’’ Southern Economic Journal 71(2005): 467–82; and Claude Berrebi (Rand Labor and Population) and Esteban Klor (Depart-ment of Economics, Hebrew University of Jerusalem), ‘‘On Terrorism and ElectoralOutcomes: Theory and Evidence from Israeli-Palestinian Conflict’’ (2004, unpublishedmanuscript).
13. See Joao R. Faria and Daniel Arce, ‘‘Terrorism Support and Recruitment’’ (2005)Defence and Peace Economics (forthcoming).
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14. For an interesting discussion of alternative policies, see Bruno Frey and SimonLuechinger, ‘‘How to Fight Terrorism: Alternatives to Deterrence,’’ Defence and PeaceEconomics 14 (2003): 237–49.
15. Due to its success, Al Qaeda has been seen by government law enforcementagencies as a strategic innovator of Clausewitzian skill (see John Upton, ‘‘In the Streets ofLondonistan,’’ London Review of Books 26, no. 2 (2004), http:==www.lrb.co.uk=v26=n02=print=upto01 .html).
16. Todd Sandler suggested this term.17. Daniel Arce and Todd Sandler, ‘‘Counterterrorism: A Game-Theoretic Analysis’’
(2004, unpublished manuscript).18. In Todd Sandler (Department of International Relations, University of Southern
California) and Kevin Siqueira (Department of Economics, Clarkson University), ‘‘GlobalTerrorism: Deterrence Versus Preemption’’ (2003, unpublished manuscript), preemption isalways undersupplied in a model in which a nation’s people and property are in jeopardy athome and abroad.
19. In Peter Rosendorff and Todd Sandler, ‘‘Too Much of a Good Thing? The Preemp-tion Dilemma,’’ Journal of Conflict Resolution 48 (2004): 657–71. The authors analyze atwo-player preemption game in which preemption has a downside by increasing grievancesand terrorist recruitment.
20. Terrorist resources involve financial resources, personnel, and infrastructure.21. Joao R. Faria, ‘‘Terror Cycles,’’ Studies in Nonlinear Dynamics and Econometrics 7,
no. 1 (2003).22. See Knut Sydsaeter and Peter Hammond, Essential Mathematics for Economic Analy-
sis (New York: Prence Hall, 2002), 344–45.23. The other equilibrium (0, 0) generates a singular Jacobian. See Alpha C. Chiang,
Fundamental Methods of Mathematical Economics, 3rd ed. (New York: McGraw-Hill,1984), 643.
24. An anonymous referee pointed out that there has been some discussion recently ofdeterring Al Qaeda by threatening to destroy Mecca using nuclear weapons. In this modeldeterrence will not prevent, at least in the steady state since dA�=dE ¼ 0, this Al Qaeda attack,nor any other terrorist attack. However, a deterrent threat, here defined as a preemptive anti-terrorist policy (see endnote 3), is effective in reducing the steady-state level of terroristattacks.
25. A caveat must apply here. According to Ethan Bueno de Mesquita (Departmentof Political Science, Washington University) ‘‘Politics and the Suboptimal Proviser ofCounterterror’’ (2005), publicly observable counterterrorist policies become ineffectivebecause terrorists can substitute tactics to evade observable counterterror.
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