Foraging Strategies of Homo Criminalis Lessons From Behavioral Ecology Wim Bernasco — NSCR, the Netherlands, IPAM 2007

Main question Does optimal foraging theory help us understand how offenders commit crimes? WARNING Psychologist talking about biology to mathematicians and criminologists

Outline Biological perspectives on crime and delinquency Foraging behavior and optimal foraging theory – a brief overview Applications to behavioral patterns in property crimes  where to search  what to choose  how long to stay  vigilance and the trade-off with safety  social foraging

Biological perspectives on crime and delinquency physiological factors involved in delinquency plant ecology (Chicago School) as a model of human populations evolutionary psychology and human behavioral ecology Marcus Felson’s (2006)

Optimal foraging theory questions How do animals search for food? What do animals eat? Where do animals eat? How long do animals stay in a patch? What affects feeding behavior?

Components of optimal foraging theory Optimization  decisions: what can be chosen?  currency: what is maximized?  constraints: within which limits? Natural selection  Optimal strategies increase fitness (survival and offspring)

Optimization in foraging theory Decisions  what to eat?  how long to stay in a patch?  where to search? Currency  long term expected energy gain per time unit while foraging  extensions : survival, defense, mating … Constraints  not eat and search at the same time  searching and handling of prey takes time and energy

Search or eat? Decision  choice = probability of eating encountered item Currency  maximize long-term average rate of energy intake Constraints  cannot search and eat at same time  encounter is a Poisson process  energy, handling and encounter exogenous  encounter without attack is free  ‘complete information’, perfect prey recognition

Search or eat (model implications) 1-0 rule  given prey type is either always chosen or never chosen, pr(attack) = 0 or 1 profitability ranking  prey types are ranked by profitability (energy/time), prey types added to diet in rank order independence of encounter rate  inclusion of prey depends on its own profitability and on profitability of higher ranked prey types, but not on encounter rate

What to steal? Specialize or not ? Hypotheses open for testing  Target choice can be broad or selective, but it is consistent (items are always or never taken)  Items are ranked in terms of profitability (i.e on basis of CRAVED)  High availability of low ranked items does not create demand  When opportunities for stealing highly ranked items decrease, offenders become more versatile (less selective)

How long to stay? Decision  choice = how long to stay in encountered patch Currency  maximize long-term average rate of energy intake Constraints  cannot search and eat at same time  encounter is a Poisson process  encounter rates with patches are exogenous  negatively accelerated ‘gain function’  ‘complete information’, perfect patch type recognition

How long to stay (model implications) marginal value theorem: leave patch when marginal rate (energy/time) drops to average habitat rate marginal rate at patch exit same for all patches visited longer in patches when travel times increase

Marginal value theorem (if all patches equal) time in patch energy travel time 1/λ 1 t1t1 1/λ 2 t2t2 λ = patch encounter rate 1/λ = expected time between patches 0

When to leave criminal target areas? Stay longer in places that are more profitable Stay longer if travel times between targets or target places are larger Stay longer if the access time and costs are higher

Central place foraging food is carried back to a central place example: birds feeding their young affects (return) travel time influences behavioral choices

Central place foraging foraging close to central place distance-size relation  short distance: light prey items  long distance: heavier prey items stay longer in distant patches more selective diet in distance patches

Central place foraging in crime some empirical evidence distance decay distance from home proceeds of offences frequency of offences distance-gain

Trade-offs single currency (energy/time) too restrictive ‘utility’ is trade-off between  nutrition value  travel time  hydration (water)  risk of predation  other important things (mating, child care) optimal decisions apply multiple criteria from a priori to a posteriori currencies

Where would you forage? YOUR NEST PREDATOR FOOD WATER

Discrete Choice Framework (RUM)  choose 1 out of J alternatives  actor i chooses j yielding max U ij (‘utility’)  U ij function of Food, Water, Predation risk, distance, random error  Food, Water, Predation risk and Distance decision criteria  , , , and  indicate direction and weight in decision (estimated a posteriori)

Discrete spatial choice model YOUR NEST PREDATOR FOOD WATER Food=16 Water = 0 Predation =3 Distance=1 Food=3 Water = 3 Predation =1 Distance=4 Food=9 Water = 1 Predation =4 Distance=3

Residential Burglary Target Area Choice Attractiveness of target areas Affluence  mean value properties  % home-ownership Lack of social control  residential mobility  ethnic heterogeneity Proximity and familiarity  proximity to home address  proximity to city center Opportunities  Number of residential units Attributes of offenders Ethnic origin  non-native versus native Age  minor versus adult

Results of Basic Model (All Burglars) Increase of In attribute Changes probability by factor 1000Residential units1.35* 10%Residential mobility %Ethnic heterogeneity1.16* €100,000Real estate value %Home ownership kmProximity1.68* 1 kmProximity to CBD0.88* Data note: police records, 269 burglars, 548 solitary burglaries in 89 neighborhoods in The Hague, The Netherlands

Importance criteria by offender types Increase of In attribute Changes Pr by factor for 10%Ethnic heterogeneity1.11natives 10%Ethnic heterogeneity1.21*non-natives 1 kmProximity1.62*adults 1 kmProximity1.96*minors

Social foraging  Robinson Crusoe models  animals often forage in groups  game theory (optimization  equilibrium)  what can we learn about crimes, offender groups, collaboration, proceeds, distances and optimal group size?

Unfortunately, not much  cooperative foraging is very rare in animals (lions, bees, ants …)  foraging apart together is the rule  animals compete over food, and even if they cooperate, they ‘cheat’ and ‘steal’ from each other

Why does this not fit (property) crime? property offending is not competitive (not zero-sum game amongst offenders) competition only plays a role in illegal markets (drugs-dealing, prostitution) interesting, but not useful in explaining causes and effects of cooperation in property offending

Discussion and conclusion Discussion  Is optimal foraging theory different from rational choice theory?  animals must eat (or die), humans may choose not to offend, they have alternatives Conclusion  OFT is useful for generating hypotheses, but apply with care

Thank you!

Distance decay and groep

Risk-sensitive foraging maximizing (long-term) expected energy gain may not be optimal