The offender is on the way home from work when his violent fantasies become overwhelming. He decides to go to a prostitute stroll that he has frequented in the past. In fact, he has abducted and killed women two other times from this area. After he finds a new suitable target, he gets her into his car, where he can control her. However, he feels too exposed to act out his fantasies because there are too many people around. Instead, he drives the woman to a secluded area outside of town where he kills her, dumps her body next to a creek, and then goes home.

By the time this series of murders ends, the offender has successfully abducted six victims. Five are killed and dumped in the same general area, and one has escaped.

This can be done without violating ethical standards, harming the community, or putting potential victims at risk. In addition, in silico assumptions can be relatively easy to review, test, and compare with reality.

Simulation

Human beings naturally engage in implicit mental simulations. They constantly build and test their own mental models to define the environment and people in it. These schemas, scripts, and internal narratives act as road maps to help people spatially and socially navigate both routine and novel situations. For instance, while in a traffic jam, individuals might mentally review and consider various alternate routes. During a meeting, they may self-censor a comment that could result in negative consequences.

To form their expectations, people think about and, thereby, informally model what they currently know and then make inferences. Investigators do this regularly when they develop crime theories or consider an offender’s potential motives. These expectations, drawn from mental simulations, constitute an important part of understanding the past, reacting to the present, and planning for the future while dealing with the unknown.

Intellectually considering possibilities and using computer simulations have remarkable similarities. In both cases, people draw from experience and research to construct meaning for what they are trying to understand. A simulation simply serves as an analogy for reality that can provide unique or unexpected insights.

Useful Models

Models are static representations of the rules that turn a series of inputs into a series of outputs. For instance, an implicit version of the routine activities theory (RAT) suggests that criminal activity occurs when a motivated offender encounters an available target in the absence of capable guardianship.² These relationships can be expressed explicitly as a set of rules, or a model. The more explicitly the model is expressed, the more practically it can be tested and used to address specific issues through simulation.

While a model defines how inputs will interact, simulation involves a dynamic test of what emerges from these interactions. Using an analogy, when someone builds a paper airplane, the person has created a model. Throwing it creates a simulation that allows the individual to see how the model performs (e.g., flies straight, curves, crashes). Likewise, a simulation based on the RAT model involves a dynamic representation of the components (e.g., motivated offenders, available targets, capable guardianship) and what patterns emerge over time when they follow rules of interaction.

To further illustrate the usefulness of in silico methodology, burglaries can be explored by expanding on the RAT model (see figure 1). To begin, RAT can be expressed as an agent-based model (ABM) of burglaries with a simple set of rules.³

• If a burglar encounters a residence without the presence of police, a burglary will occur.
• If a burglar encounters a residence with the presence of police, a burglary will not occur.

A logical follow-up question to these two different model revisions is, what if both deterrence and interdiction are part of the same strategy? To answer this question, the model can be set to include both deterrence (the burglar sees police if within 150 feet) and interdiction (police detect the burglar if within 300 feet). Running this revision across all 100 virtual communities results in an average of—

• 77 burglaries committed,¹³ of which 38 resulted in interdiction;¹⁴ and
• 248 burglaries prevented.¹⁵

The 248 prevented burglaries fall into two categories—those stopped by deterrence (21 times)¹⁶ and burglaries indirectly prevented because of interdiction and arrest (227 times).¹⁷

The important takeaway from each of these simulations is how consistently the numbers change across the 100 virtual communities given the parameter changes.

Real-World Applicability

The results from the above burglary model may not immediately transfer to the real world due to factors that the model does not account for, such as actual street layout, differing levels of offender motivation, and the presence of target hardening. In fact, a common criticism of some in silico methods is that simple models do not offer a level of complexity useful or applicable to real-world scenarios or circumstances. However, even a simple model with subsequent simulation can become a powerful tool by illustrating useful outcomes and posing relevant questions.

For instance, given the aggregated results of the burglary model, it is interesting to note that the combination of interdiction and deterrence actually results in a higher average number of burglaries than interdiction alone. Upon further reflection, this unexpected result does make sense. If burglars have an opportunity to avoid interdiction by observing that there are police in the area, they simply defer their activity until the police leave. These burglaries have not been prevented, just delayed.

This point derived from the burglary model highlights a deeper issue—capable guardianship is not always a permanent deterrent. Police presence alone does not reduce the motivation for burglary in the long term. On the other hand, while interdiction and arrest may not affect motivation, they do diminish opportunity.

Even this simple burglary model and simulation can provide a foundation for additional complexity. Simple changes may lead to powerful and interesting observations. For instance, with relatively little effort, the burglary model could be modified to include a depiction of burglars with diverse and dynamic motivations based on factors like age, drug use, time of day, or even level of “thrill” involved. Adding more specific geospatial and temporal features also could help an agency understand how the factors in the model might play out in their own jurisdiction.

One reason for using simulations in law enforcement is that it encourages problem-solving by allowing users to make mistakes and see the potential consequences of those mistakes in a safe environment. This explains why simulation has gained a foothold in training. The use of live scenarios, shooting simulators, and driving simulators gives trainees the opportunity to test their assumptions and adjust based on simulation feedback.

A “test-retest-decide” methodology in silico can benefit operational anticipation and planning by allowing an agency to define and refine intended consequences and foresee and mitigate potential unintended ones. This can be applied at a variety of scales within a law enforcement environment, from administrative policy to strategic initiatives.

Within behavioral analysis, in silico techniques provide an opportunity to create artificial populations of offenders, especially in low-base-rate events like serial homicide. The reason to generate these populations is not to predict any specific offender’s actions or behaviors, but rather to test theories about offending.

Agencies can identify their own complex issues that may benefit from being addressed in silico. For instance, traffic enforcement represents one of the biggest challenges facing many police departments. In these instances, implementing a traffic simulation could foster a complex understanding of vehicle flow at problematic intersections, identify and address roadways with high accident rates, or even help plan for traffic issues that emerge during large-scale entertainment or sporting events.

Many other potential applications of in silico methods exist. A few examples serve to demonstrate.

• Creating a system dynamics (SD) model¹⁸ of an agency and then stressing it can test the resilience of the organization’s structure when two or more major events occur simultaneously.
• Using an ABM¹⁹ might help a correctional facility anticipate the point at which a riot may emerge from inmate and correctional staff interactions.
• Employing a social network analysis (SNA) model²⁰ to simulate a criminal organization’s network of actors, skill sets, and assets may facilitate predicting that organization’s reaction to the elimination of an income stream. Does the group become crippled, refocus on a different income source, or become more violent and territorial?

Countless complex subjects exist that, although ripe for analysis, are difficult to measure and observe or do not allow for the ethical collection of data. Understandably, police executives in the real world do not want to stress their agency to see what breaks it, correctional facilities do not want to incite a riot, and many interdiction efforts can have a wide range of costly unintended consequences.

Resources

Modeling and simulation resources can take on a variety of scales and costs. However, in silico methods can help conserve resources and reduce expenditures overall. As such, in silico efforts depend on the 1) specific need and 2) human expertise available to engage in the project.

To the first point, it is surprising what someone can accomplish using a simple spreadsheet, especially when implementing an SD model. Even so, more specialized models, like ABMs or SNAs, can be created with either open source or relatively inexpensive software.²¹

Regarding the second point, when engaging in an in silico effort, it is most important to involve personnel who can create a model quickly and efficiently and employ it in an explicit and collaborative way. This requires individuals willing to be creative and push the boundaries of software suites that many departments already have in place.²²

Agencies also may have access to outside sources of expertise. For instance, local or state planning departments may be using simulations for traffic planning or infrastructure development and could serve as a potential source of expertise. Further, agencies might want to consider reaching out to local universities and research centers that have strong computational modeling, simulation, or computer science departments.

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