Social control theory is based on the assumptions that

Abstract

Control theories of crime focus on the inhibiting effects of conventional social institutions on criminal behavior. In general, control theories of crime emphasize how strong social ties to institutions, such as one's family (e.g., parents, spouses, and children), peer group, school, church, community, and workplace, among others, are expected to reduce the likelihood of crime by highlighting the negative consequences of criminal acts for those social ties. The cumulative body of contemporary criminological research is largely supportive of the key expectations of control theories of crime.

Abstract

Robust control theory is a tool for assessing decision rules when a decision maker distrusts either the specification of transition laws or the distribution of hidden state variables or both. Specification doubts inspire the decision maker to want a decision rule to work well for a ∅ of models surrounding his approximating stochastic model. We relate robust control theory to the so-called multiplier and constraint preferences that have been used to express ambiguity aversion. Detection error probabilities can be used to discipline empirically plausible amounts of robustness. We describe applications to asset pricing uncertainty premia and design of robust macroeconomic policies.

Self-Control Theory in a Nutshell

SCT proposes that the behavioral outcome of a self-control episode is determined by the interplay of seven core psychological components which could be represented as nodes in a graph (see Fig. 19.1):

Desire. A driving force which begins as a subcortically mediated visceral state of “wanting” (as defined by Berridge, Robinson, & Aldridge, 2009), often followed by cognitive elaboration, which directs a person toward immediate reward-related stimuli.

Higher-order goal. A more cortically mediated and largely cognitive construct associated with an endorsed end state that motivates instrumental psychological (cognitive, affective, and behavioral) activity. Unlike desires, higher-order goals are often pursued intentionally and associated with declarative expectations of long-term benefits.

D-G conflict. A form of response conflict caused by the coactivation of a given desire and an at least partly incompatible higher-order goal. D-G conflict turns desire into temptation and the higher-order goal into a self-control goal.

Control motivation. The aspiration to control desire. As such, control motivation is determined by the self-control goal as well as additional factors that increase this aspiration.

Control capacity. All the potential nonmotivational cognitive resources a person can use to facilitate the control of temptation (eg, directed attention and inhibitory capacity).

Control effort. The effective use of control capacity.

Enactment constraints. Environmental factors that limit one’s behavioral options.

In a nutshell, SCT proposes that the first three components—desire, higher-order goal, and D-G conflict—are involved in activating self-control (activation cluster): A desire (eg, for relaxation) in itself is unproblematic and perhaps fully endorsed. It is only when an incompatible higher-order goal (eg, to meet a tight deadline) is present that the desire becomes a temptation and the higher-order goal becomes a self-control goal. The extent of D-G conflict experienced is a function of the strength of the desire, the strength of the higher-order goal, and the degree to which they are incompatible. D-G conflict activates self-control exertion by triggering control motivation. Control motivation and control capacity are major determinants of control effort. Together, these three components form the exertion cluster. Higher control motivation and control capacity yield higher potential control effort—the amount of control effort that one is prepared to spend toward combating temptation. SCT proposes that the strength of temptation, the perceived skill with which one can handle said temptation, and competing goals determine actual control effort—the amount of control effort that one actually uses to effectively combat temptation. If the investment of actual control effort reaches a threshold to prevail over desire strength, then self-control will “succeed” (ie, temptation will not be enacted), provided that enactment constraints do not prevent success. If actual control effort does not reach this threshold, then self-control will fail (ie, temptation will be enacted), provided that enactment constraints do not prevent failure.

What Is Distinctive About Leadership for Security Operations?

The essence of security management traditionally relates to the appropriate creation, imposition, and implementation of controls over personal behavior. Other management disciplines market and sell, finance, make, move, and administrate products or services. While security management relates to all of these, the discipline is primarily oriented in manipulating behavior to reduce or eliminate loss. To accomplish this, the operations security manager considers appropriate controls that can achieve the desired objectives within the context of the organization’s total operations.

Security operations managers concentrate on internal and external controls because they are more amenable to change than are other factors. For example, genetic factors seem to play a role in crime causation: men disproportionately are more responsible than women for serious violent and property crime. Yet some women commit such crimes, and it is not reasonable or logical for a security manager, say, to urge that mostly women be hired because they are less likely to be involved in crime. Both men and women are needed in workplace. Similarly, employers do not select workers based on any narrow set of environmental preconditions. Most flourishing workplaces thrive when employees represent diverse social, ethnic, and national backgrounds. This means that managers must direct loss prevention while working with all types of people.

Control theory emanates from the work of Emile Durkheim, a suicidologist who concluded that the control and discipline of one’s desires and the subordination of inclinations to the expectations of others stem from group integration and its intensity of involvement over behavior.15 Those prone to suicide lose this control. Durkheim’s work influenced Travis Hirschi’s seminal work, Causes of Delinquency, which assumed that antisocial acts occur when an individual’s bonds to society are weak or broken.16 Hirschi’s work centered on violence and property crime. Another theorist has concentrated on crime not in the streets, but in the suites. Edwin H. Sutherland, a pioneering sociologist at the University of Chicago, first named and described “white-collar crime,” which is crime of a substantially different nature than street crime.17

Often referred to as “crime in the suites” or “gray crime,” nonviolent financial crimes often escape prosecution, but are an utmost concern to corporate security practitioners.

Yet although these prescient observers’ theories added to the understanding of deviance in the workplace, they provided little in the way of guidance to managers who sought to reduce loss. Other researchers would later fashion practical measures to deal with situational incidents that could be anticipated and controlled or resolved.

The security operations manager plans to respond to actual or reasonably possible situations by establishing situationally appropriate control measures. Since such measures generally cannot guaranty certainty of success, the manager also must be prepared to respond with alacrity once normative violations occur in the workplace.

The phenomenon of control

Based on general analysis given in the previous section, we can now begin to characterize the phenomenon of mind as a phenomenon of control. The proper setting for such an analysis is within control systems theory and practice. This is a discipline based on dynamic systems theory and specifically focused on the dichotomy of body–mind, or plant controller, in our case.

Control theory (Ogata, 1990), as understood in the control world, is a deeply theoretical, mathematical endeavor. Control engineering is the engineering side where the theoretical results are put into practice in the form of controllers for machines and processes. There exists, however, a big gap between theory and practice—as is the case in all engineering disciplines:

The theoretical results may turn out to be non-applicable for several reasons, among which there are: lack of understanding by practitioners, excessive constraints for their application, lack of plants matching the theoretical models, etc.

There are domains of control technology lacking in theory. This lack may be due to missing interest on the part of control theorists (e.g., sensor drift problems) or may be due to a lack of an adequate formal model (e.g., human supervisory control).

In a sense, control theory has been driven by its mathematics, reaching a situation very similar to that of pure mathematics: disconnection from the real world. And, like most theoretical endeavors, it suffers from the “Consider a spherical cow ...” syndrome, producing solutions for yet-to-come problems. On the other side, control practice suffers from generalized under-training and lack of rigor in many of its activities. This is a purely economic management issue, because reasonably good solutions are enough for the real world.

In control systems analysis and design, the term “plant” is used to refer to the system we are interested in controlling and the term “environment” is to refer to the rest of the universe (Figure 20.4). Obviously, there are interactions between the plant and the environment which affect both the dynamics of the plant and the environment. We are interested only in isolated systems as degenerate theoretical cases of this interaction. The interaction can be classified into three categories:

Outputs: The quantities2 coming from the plant that we are interested in. This could be, for instance, the production level in T/h in a cement plant or vehicle speed in a cruise control system.

Inputs: The quantities that we can manipulate to drive the plant to the operational point we are interested in. In the previous examples, these would be the coal burning rate or the position of the car throttle.

Disturbances: They are material or energetic flows from the environment that cannot be controlled but nevertheless affect the plant's operation. Examples of this are the level of humidity in raw materials or the force of the wind in the road.3

The phenomenon of control can be simply stated (Figure 20.5) as:

If the dynamics of the interaction of a plant with its environment is not as desired—in terms of some observable quantities—it is possible in general to complement the system with an additional subsystem—a controller—so that the resultant dynamics of the system plant+environment+controller renders the desired dynamics at the target quantities.

The task of devising the appropriate controller for a given plant and a given set of objectives is called control design. In the case of biosystems, the “designer” is evolution. This is apparently non-teleological but if analysed in detail is exactly equivalent to a teleological mechanism addressing selfish gene objectives (Dawkins, 1976). The control design problem is an inverse mathematical problem that can be exceedingly difficult to solve (and indeed is insolvable in many cases). The common strategy to achieve a solution in difficult cases is dual: it tries to simplify the mathematical problem by making approximations4 and relaxing the target objectives.

We have said that it is possible “in general” but not “always” to complement a plant with a controller so as to reach a concrete global dynamics, because in some cases the necessary controller is physically unrealizable (e.g., would require non-causal behavior). The process of realization comes after the design phase and is called controller implementation. Some of the implementational problems are directly addressed in the design phase (e.g., non-causal controllers are not considered acceptable designs) but other implementational problems cannot be handled in the design phase. The principal reason is that the construction process cannot normally be sufficiently formalized to be of use in the design process, which is deeply mathematical.

The most common implementational strategy today is the construction of controllers as software, as sets of interacting programs. So ultimately, the resulting mind controlling the physical body of the plant is a collection of interacting software processes—this being a precise term in computer science— running atop some computer and communications hardware. The discipline of control engineering has become a discipline of control+computing+communication. The computer metaphor for the mind is no longer a metaphor (Searle, 1990; Cisek, 1999); it is a technological asset.

2 Connections with the Calculus of Variations and Dynamic Programming

Optimal control theory has its origin in the classical calculus of variations, which concerns essentially the same type of optimization problems over time. However, with the maximum principle, developed by Pontryagin and his associates (see Pontryagin et al. 1962), the class of problems that optimal control theory can deal with is much more general than the class solvable by the calculus of variations.

Optimal control theory is formulated in continuous time, though there is also a discrete time version of the maximum principle; see Léonard and Long (1992). In discrete time, the most commonly used optimization technique is dynamic programming, developed by Bellman (1957), using a recursive method based on the principle of optimality. Bellman's principle of optimality can also be formulated in continuous time, leading to the Hamilton–Jacobi–Bellman equation (or HJB equation for short), which is closely related to Pontryagin's maximum principle, and is often used to provide a heuristic proof of that principle (see Arrow and Kurz 1970). Such a heuristic proof reveals that the shadow price (i.e., the co-state variable) of a given state variable is in fact the derivative of the value function (obtained in solving the HJB equation) with respect to that state variable, thus justifying the name shadow price. This approach yields equations that are in agreement with (b) in the preceding section.

3.2 Promising Trends

Control theory refers to informal social control, leaving aside the law and other formal controls. This is not to say that the latter are neglected in contemporary criminology. On the contrary the focus is increasingly on official controls and the evaluation of their effectiveness. The revival of deterrence theory is a case in point. In its classic formulation, the deterrence hypothesis predicts that the frequency of crime will vary inversely with the certainty, celerity, and severity of punishment handed by the state. Recent researchers cannot find much deterrent effect of severity on crime, but crime rates tend to be low when the certainty of punishment (often measured by the police clearance rate) is high. It is also the case that there is much more evidence of a general effect of certainty on crime rate (general deterrence) than on the level of recidivism of punished offenders (specific deterrence) (Andenaes 1974, Gibbs 1975, Blumstein et al. 1978, Cook 1980, Nagin 1998).

However, there are other ways to limit crime than deterrence and informal controls. The state uses nonpunitive ways to keep crime at bay, like therapies to rehabilitate or reform offenders and crime prevention measures such as street lighting, surveillance of public areas, gun control, and regulations on car antitheft systems. In civil society, crime control activities are very common. Everybody devotes time, money, and effort to protect their goods against theft and to safeguard their person against aggression. People put their money in banks, lock the doors of their houses, avoid dangerous places; in some cases, they even buy guns for self-protection. Businesses and other organizations spend considerable amounts of money to protect their assets from loss or crime: They hire private guards, control access to their premises, use safes, install CCTV, etc.

All those actions and precautions—public and private, formal and informal, repressive and preventive—are clearly aimed at reducing the probability of a crime occurring. As such, their common goal is crime control. This leads us to a last definition: the social control of crime refers to all the means specifically aimed at reducing the probability or severity of crime.

The reader will note that social control is now defined by its intention or aim, not by its results. By doing this, we follow Gibbs (1989, pp. 23–4) who criticizes the sociological concept for belittling the intentional quality of social control. In its common use, the term ‘control’ conveys intention: one tries deliberately to control, to direct, to influence another. Actions having the unintended effect of preventing crime no doubt exist. For example, Felson (1998) explains the sharp decline of crime rates starting in 1994 in the USA by the coming of a cashless society. People use more credit cards and the like, so they have less cash in their pockets and their houses. Having less cash to steal, the offenders become less active. In this instance, we should not speak of social control but of an unintended preventive effect of an economic evolution.

Results (more or less crimes) are important matters but should not be included in the definition of social control. Attempts at social control, including failures, are social control. The latter's impact is not a matter of definition but of evaluation. Because of their exclusive focus on scientific evaluation, Sherman et al. (1998, p. 2) chose another path. They define crime prevention not by its intention but by its consequences. It is ‘any practice shown to result in less crime than would occur without the practice.’

If social control is made of intentional actions and choices, is it possible to conceive its impact on offenders in terms of actions and choices? The potential offender—that is, the individual having the intention to commit an offence—might choose to do it despite social controls or not to do it because of them. Such an individaul is a decision maker acting under the constraints of social control.

The impact we try to have on offenders when we attempt to control crime is essentially to: (a) increase the effort of commiting crime (e.g., by target hardening or gun control); (b) increase the risks (by surveillance, punishment, burglar alarms, and the like), (c) reduce the anticipated rewards of crime (by target removal, identifying property, etc.), and (d) removing excuses used by offenders to minimize the moral opprobrium cast on crime (e.g., by rule setting and public condemnation of crime) (Clarke 1997). If potential offenders live in a well-ordered society where those impacts are achieved, they will find themselves in a radically different choice situation than in a disorganized society where social controls are erratic. Most of the time they will find commiting crime difficult, risky, unrewarding, and reprehensible. If they are minimally rational (Cornish and Clarke 1986), they will tend to look for noncriminal alternatives. This means that where and when the social controls operate reasonably well, they shape the alternatives of choice for social actors. They close most of the criminal options for of us. They attach quite negative utilities (in the economic sense) to the criminal options. To commit crime in such a situation, one needs a fair amount of greed, temerity, disregard for long-term consequences, or plain foolishness.

Logically, the more serious a type of crime is, the more it should be worthwhile controlling. In fact, we find that guardianship is lax when minor valuables are to be protected and increased when major valuables or life must be protected. Police detectives work harder investigating murders than burglaries. The positive relation between the gravity of crime and the severity of punishment, as well as between the gravity of crime and the certainty of punishment are basic facts of research on penal decision making (Gottfredson and Gottfredson 1980). This heightened pressure of social controls on the most serious crimes will give incentive to offenders to choose the least criminal option, the least severe offence in the eventuality that they persist in crime. That should explain the inverse relation one finds between the frequency of a type of crime and its severity (there are fewer murders than robberies, and fewer robberies than burglaries). Those pressures on criminal choices can be called the structuring effects of social control (Cusson 1993).

However, the lesson learned by sociologists and historians, showing that social controls often operate in an erratic manner, should not be forgotten. Formal and informal controls are not in place where they should be for a number of reasons: groups are too disorganized, resources are lacking, the acts do not follow the rhetoric. This is to mean that the quality and intensity of social control have every reason to be highly variable in space and time. In turn, this uneven quality and intensity of social control should not be unrelated to the uneven distribution of crime rates in space and time.

C Social control theory

Social control theory suggests that the establishment of a social bond is a psychological condition that buffers against risk factors in life (Hirschi, 1969). According to Hirschi (1969), attachment to a positive institution, commitment to conventional pathways of achievement, and beliefs in the legitimacy of societal order are key elements in establishing a social bond. An established social bond exerts effective control on deviant behavior (Catalano, Haggerty, Oesterle, Fleming, & Hawkins, 2004). However, weak social bonds may result in increased risk for delinquency or crime. Social control theory researchers often choose to study school bonding and school connectedness, which overlap with the emotional aspects of school engagement.

Hawkins and Weis (1985) defined school bonding as attachment to prosocial peers, commitment to academic and social activities at school, and belief in the established norms for school behavior (Simons-Morton, Crump, Haynie, & Saylor, 1999). Similarly, Resnick et al. (1997) focused on how school connectedness can protect adolescents from harm. As a possible protective force for youth, school connectedness may buffer against delinquency and deviant behavior, may act as a preventive force for school dropout, and may provide protection from negative influences.

Social control theory has been modified and developed into a slightly different theory—a social development model (e.g., Catalano & Hawkins, 1996; Hawkins, 1997; Hawkins & Lishner, 1987). Integrating social control and social learning theories, the social development model hypothesizes that strong bonds of attachment and commitment can only be formed when “reinforcements” from the environment for desired behaviors are available; these reinforcements are needed so that youth have positive experiences with interacting with other members of the social group (Hawkins & Weis, 1985). That is, social bonding or attachment is developed when behaviors that are consistent with conventional standards are rewarded consistently through a positive reinforcement process. The development of a strong social bonding further increases positive behaviors and prevents problematic behavior (Hawkins, Guo, Hill, Battin-Pearson, & Abbott, 2001).

Social Control Theory

Social control theory holds that adolescents will engage in deviant behavior unless bonded to conventional societal institutions, such as family, schools, and religion, and to conventional role models such as parents and teachers. Adolescents with weak bonds to conventional institutions and role models fail to internalize the values held by conventional society. Such failure to form strong bonds may result from strain caused by poor relationships with parents or a discrepancy between an adolescent's goals and perceived ability to reach those goals. Social disorganization (the breakdown of established institutions that provide social control) may similarly result in failing to bond with conventional institutions and values. Adolescents without goals or family strain and with intact societal institutions available may not have been adequately socialized to adopt societal values, and may therefore turn to deviant peers, facilitating the development of substance use.

The Cost-Function-Based Solution to the DoF Problem: Optimal Control Theory

Optimal control theory is a classical engineering design technique for controlling complex systems in which infinite solutions are possible, given a desired task or behavior. The general idea is that in order to design the best possible controller of a system, capable to carry out a prescribed task, one should define first a ‘cost function,’ that is, a mathematical combination of the control variables that yields a single number (the ‘cost’ of the action). This function is generally composed of two parts: a part that measures the ‘distance’ of the system from the goal of the action and a part (regularization or penalty term) that encodes the required ‘effort.’ The design is then reduced to the computation of the control variables that minimize the cost function, thus finding the best possible trade-off between accuracy and effort.

The first attempt to apply this approach to human motor control was carried out by Flash and Hogan (1985), by proposing the ‘integrated jerk’ as the regularization term of the cost function. ‘Jerk’ is the time derivative of acceleration, and thus minimizing jerk is equivalent to maximize the smoothness of the generated trajectory. They showed that the solution of such minimization tasks for point-to-point, planar reaching movements was indeed consistent with the spatiotemporal invariances found by Morasso (1981). However, other simulation studies found similar results by choosing different types of cost functions, such as ‘integrated torque change’ (Uno et al., 1989) or ‘motor noise dependent statistics’ (Harris and Wolpert, 1998). Thus, it is not clear how to identify the cost function, supposedly used by the brain, because different alternatives tend to yield similar behaviors.

In this line of research, optimal control concepts were used for deriving offline optimal control patterns, to be employed in feed-forward control schemes. A later development (Todorov and Jordan, 2002) suggested using an extension of optimal control theory that incorporates sensory feedback in the computational architecture. In this closed-loop control technique, a block named ‘control policy’ generates a stream of motor commands that optimize the predefined cost function on the basis of a current estimate of the ‘state variables’; this estimate integrates in an optimal way (by means of a Kalman filter) feedback information (coming from delayed and noise-corrupted sensory signals) with a prediction of the state provided by a forward model of the system's dynamics, driven by an ‘efference copy’ of the motor commands. One of the most attractive features of this formulation, in addition to its elegance and apparent simplicity, is that it blurs the difference between feed-forward and feedback control because the control policy governs both. On the other hand, the mathematical computations that need to be carried out in order to identify the optimal control policy are quite complex and do not scale up well with dimensionality. Moreover, optimal feedback control requires indeed feedback, and thus is unable to treat overt and covert actions in a uniform way.