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5/11/2015-1 Institute for Defense Analyses 4850 Mark Center Drive Alexandria, Virginia 22311-1882 An Empirical Model of the Psychology of Deterrence: Reality.

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Presentation on theme: "5/11/2015-1 Institute for Defense Analyses 4850 Mark Center Drive Alexandria, Virginia 22311-1882 An Empirical Model of the Psychology of Deterrence: Reality."— Presentation transcript:

1 5/11/ Institute for Defense Analyses 4850 Mark Center Drive Alexandria, Virginia An Empirical Model of the Psychology of Deterrence: Reality Does Not Conform to Theory Robert Anthony 20 November 2004 The views expressed in this talk are solely those of the author and do not represent an official position of IDA or its sponsors. Copyright, the Institute for Defense Analyses, 2004 National Institute of Statistical Sciences Workshop on Statistics and Counterterrorism

2 5/11/ Definition of Deterrence Deterrence is “the prevention from action by fear of consequences – deterrence is a state of mind brought about by the existence of a credible threat of unacceptable counteraction.” (JCS Pub 1-02)

3 5/11/ Key Points Deterring suicide terrorists Deterrence aspects of counter-terrorism (CT) Calibrated model of psychology of deterrence –Opinions of incarcerated drug smugglers –Peru to Colombia air bridge denial operations –Caribbean drug smugglers (air or go-fast boat) –Fisheries enforcement by U.S. Coast Guard –Early automobile driving – an “extreme” sport Failures of risk perception / acceptance theories –Expected Utility Theory (EUT) –Others Perpetrators consider risks before rewards Unique property of IDA model  Universality?

4 5/11/ Deter Suicide Attacks? A careless terrorist is a non sequitur What might suicide terrorists fear? –Failure, arrest, or loss of life without completing the mission –Dishonoring or bringing retribution upon their families –Embarrassing their cause and supporters of their cause –Revealing any larger scheme and their supporting network Reframe* the terrorists’ perceptions –FROM:Avoiding a loss  RISK SEEKING –TO: Pursuing a gain  RISK AVERSE * Tversky and Kahneman (1981)

5 5/11/ How Does Deterrence Contribute to CT? Interdict Protect Mitigate Deter Deterrence protects some unknown vulnerabilities Effective interdiction and protection will deter Deterrence is a large force multiplier: Deterred = 2 to 10 x Interdiction

6 5/11/ Rockwell interviews of inmates Opinions of incarcerated smugglers: What else matters? Nationality: U.S., Mexican, Colombian Ages: 20 to 50 Education: grammar school to Ph.D. Drugs: marijuana, cocaine, heroin Experience: 1 to 10 loads No rewards for being interviewed 254 prisoners eligible; 112 participated Experienced drug enforcement interviewers

7 5/11/ Willingness to Smuggle - Interview Results “I would not smuggle drugs into the U.S. if my chances of getting caught (caught and convicted, or caught, convicted, and imprisoned) were… ” * Certain: the last response category combines 1) not willing to smuggle when faced with certain capture and 2) willing even if certain to be captured.

8 5/11/ Inmate Willingness to Smuggle Chi-square probability = 0.49 Interview data Associate caught Assoc. imprisoned Self caught Self imprisoned Willingness Function Deterrence threshold “Undeterrable” residual

9 5/11/ Fisheries Violations & U.S. Coast Guard Effort Willingness = Violation Rate (VR); Interdiction = Boarding Rate (BR) BR2[B(Nnwr)/EV] VR (Nnwr) South Atlantic and Caribbean y = 3E-07x ±0.16 Exponents are indistinguishable from -1.0.

10 5/11/ Early Automobile Use Source: Starr, 1972 Percent of U.S. Population Using Automobiles Fatalities per Person Hour of Exposure tail Note: on log-log plot, inverse power is 45  line

11 5/11/ Peru to Colombia Air Bridge Denial Operations Observed Willingness to Smuggle Observed Probability of Interdiction Loss of drugs, vehicle, and chance of imprisonment Incarceration Lethal Consequences Peru ABD non-lethal ops Peru ABD lethal ops Transition period (average)

12 5/11/ Operations against Caribbean Smuggling Loss of drugs, vehicle, and chance of imprisonment Incarceration Lethal Consequences Interdicted fraction of all smugglers Transit zone air Interdiction Puerto Rico go-fast ops

13 5/11/ Wage: What Is Enough? Traffickers assess risk first, and then ask whether the wage is enough. Saturation Factor of Increased Risk Factor of Increased Wages  R

14 5/11/ Expected Utility Theory (EUT) Leading EUT method for quantifying the psychology of risk-taking, the Conjoint Expected Risk (CER), is: Problems with EUT and risk perception theories in general: –Model coefficients change per experiment (not universal) –Mathematically inconsistent with willingness function’s –Narrow mid-range of risks in published papers –Laboratory data (mostly student trials) –Fundamental unresolved issues in academic community Willingness model fits data from one published paper better than EUT or CER

15 5/11/ Willingness Function Contradicts EUT & CER The general EUT form cannot approximate the willingness function’s form. For a fixed loss, and become a single constant,. And becomes increasing function,. And W = P 0 at P I = 1.0 implies. Also, W = 1.0 for P I = P 0 ; therefore: Because is a monotonically increasing function of P I. For To represent the “undeterrable” residual, neither EUT or CER ever drops below –45  diagonal.

16 5/11/ Data from Business Investment Experiment No. 27 Risky Situations (Two shown) Risk Ratings (Scale 0 to 10) Accepted GainLossAvgStd W … Range 0.3; 0.7+2; +48-2; ; ; ; 0.97 Source: J. Sokolowska, A. Pohorille / Acta Psychologica 104 (2000) MBA Student Responses

17 5/11/ Comparison of Models Threshold of required dropping two events with. RMSE = Root Mean Square Error

18 5/11/ Perpetrators Consider Risk First Prisoner willingness no wages mentioned Prisoner wage sensitivity ends at willingness asymptote Raw risk estimate better than Expected Utility Theory (EUT) (might also reflect incorrect functional form for EUT) “Explains” framing – Focus on fear of loss of control: A. Unless one takes an extreme risk B. If one ventures from acceptable position Persuasive Argument Theory (PAT) reinterpreted –“Successes” (attempt rate) is ignored –Number in pool = arguments for attempting –Prospects of interdiction = arguments against Weighted comparison

19 5/11/ Is Willingness Function Universal? Real-world data: –Variety of situations and cultures –Risks to well being versus monetary rewards Wide dynamic range: Simple inverse function Verified by interview comments: –Interdiction threshold of deterrence at –“Undeterrable” residual Risk decision preceding reward decision Limitations –Only fits extreme cases assuming sufficient rewards –Fitted rather than function for consequence severity

20 5/11/ Implications for Counterterrorism Deterrence leverages interdiction by large factors Perpetrators ignore interdiction risks below the deterrence threshold – implies a minimum investment to be effective Undeterrable tail of willingness function: –A contradiction to current risk assessment theories –An irreducible threat to interdict? –Understanding why there is a tail may be key to understanding the psychology of the perpetrators If the willingness function is universal: –Terrorism is an attempt to deter us (our psychology applies) –Terrorism can be compared to extortion and ethnic cleansing

21 5/11/ Backup Slides Compensation for trafficking risks Unique Feature of Willingness Function Deterrence Theory Validate Universality Terrorism = Deterring Us? Peru Data – Traffic and Interdictions

22 5/11/ Judge Risk First, Wage Second “SELF” responses require more wage to maximize willingness. PoPo PoPo reveals need for “sufficient wage”. Baseline risk Willingness judgments assumed a sufficient reward: risk decision precedes reward decision.

23 5/11/ Unique Feature of Willingness Function Deter Interdict They Succeed Willingness Probability of Interdiction

24 5/11/ Deterrence Theory Joint IDA / academic research on deterrence theory (Risk perception / decisions, psychophysics, etc.) Need for theory –Explain “undeterrable” effect and develop CT options –Preventing terrorists from deterring us –Understanding communication about risk –Development of MOPs »Indirect measures, e.g., communication content »Find more direct observables

25 5/11/ Validate Universality Verify and calibrate deterrence model for past cases –Terrorism – Intelligence Community data –Populations being terrorized – Open and classified sources –Professions or sports with extreme risk – Actuarial data –Other? Reanalysis of military surrender –Casualty rate –Framing perceptions of battle consequences

26 5/11/ Terrorism = Deterring Us? Terrorism as deterrence of peoples & governments –Northern Ireland – western society & years of conflict –Bosnia & Kosovo – ethnic cleansing displacements –Afghanistan – casualties, intimidation, & displacements –Colombia – paramilitary forced relocations, FARC assassinations of police & politicians –Criminal extortion – force necessary to gain compliance Recasting willingness function from terrorist view –Verify willingness function based on level of violence –Thresholds for various consequences

27 5/11/ Peru Data – Traffic and Interdictions NAS = Narcotics Affairs Section of U.S. Embassy Peru


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