A Behavioral Justification for Punishment Escalation Schemes

A Behavioral Justification for Escalating Punishment Schemes

Course Date Student Instructor Affiliation
A Behavioral Justification for Punishment Escalation Schemes
Introduction
The vindication of a specific action or policy based on the behavior of those who engage in it is referred to as behavioral justification. This justification can come from both within the organization enforcing the punishment scheme and from outside sources. Punishment schemes in the United States frequently face public criticism for being overly punitive (Miceli, 2013). A recent study, for example, found that a majority of Americans opposed raising the penalty for possessing small amounts of marijuana from a misdemeanor to a felony. The concept of deterrence is one justification for escalating punishment schemes. The theory of deterrence is based on the idea that by imposing an appropriate punishment, criminal behavior can be reduced. In other words, if someone knows they will face severe punishment if they engage in criminal behavior, they are more likely to refrain.
Deterrence is justified by two principles: the principle of prevention and the principle of retribution. According to the prevention principle, if we can keep criminals from committing crimes in the first place, we will have successfully reduced the number of criminal acts. According to the principle of retribution, if someone is punished for their actions, they are more likely to behave morally in the future (Endres & Rundshagen, 2012). Deterrence theory has been shown to be effective in a variety of situations. For example, when enacting drunk driving punishment schemes, the principle of deterrence is effective in reducing the number of drunk driving incidents. Similarly, the deterrence principle is effective in lowering crime rates among juvenile offenders.
Theoretical Foundations and Current Research
Many governments have turned to escalating punishment schemes in the modern era of crime to deter offenders and send a message to them. According to the theory behind these schemes, as the severity of the punishment increases, so does the likelihood that the offender will stop committing crimes. Escalating punishment schemes are used to deal with offenders who have shown a disregard for the law. The idea is that as the severity of the punishment increases, the offender’s behavior will change. This is because they will be aware that if they continue to commit crimes, they will face unpleasant consequences (such as imprisonment). A number of studies have been conducted to examine how escalating punishment schemes work in practice, and the majority of them indicate that they are effective in deterring offenders (Endres & Rundshagen, 2012). The proposed study employs the symbolic interactionism approach to explain how escalating punishment schemes work. This theory proposes that people interpret the actions of others in order to comprehend the social world around them. The way people use symbols to communicate with one another results in symbolic interactions. This means that incarceration will be interpreted as a sign that the government is serious about punishing offenders. This is likely to change their behavior because they will realize they are putting themselves in danger if they continue to commit crimes.
A model and an optimization analysis will be used.
Mungan (2014) empirically investigates the effects of escalating punishment schemes on crime using a decision tree model of potential offenders. The model considers the offender’s characteristics, such as their criminal history and willingness to commit crimes. The model includes several adjustable variables, such as the severity of the punishment, the number of crimes committed, and the likelihood of being caught. The punishment’s severity is a dependent variable, and the greater the severity, the greater the deterrence effect. The number of crimes committed is also a dependent variable that has a direct impact on the level of deterrence. The probability of being caught is a control variable that modifies the deterrence effect. Given their unique characteristics, the model can calculate the optimal punishment scheme for each offender.
She begins by discussing the uniform sanction scheme, in which each crime receives the same punishment. In this case, there is no incentive to commit fewer crimes to avoid being caught, so the number of crimes committed remains constant. She then considers escalating sanction schemes, in which the penalty for each crime becomes harsher as the number of crimes committed increases (Mungan, 2014). In this case, there is an incentive to commit fewer crimes in order to avoid being caught, which reduces the number of crimes committed. Finally, she considers the case in which there is no sanction scheme. There is no incentive in this case to commit fewer crimes in order to avoid being caught. Other scenarios in which there may be an incentive to commit fewer crimes include punishment that is proportional to the number of crimes committed or punishment that varies depending on how many times an offender has been convicted.
Analysis of Optimality
If this measure is maximized, a uniform action scheme is said to be optimal. In the absence of a sanction scheme, Z(s) = 0. However, in the other three cases where a sanction scheme exists, Z(s) > 0, indicating that the measure of crimes committed is not maximized. If the sanction scheme is lenient in the first case, then (Z(s) 0). This happens because there is a financial incentive to commit fewer crimes in order to avoid being caught under a lenient punishment scheme. In the second case, where the sanction scheme is lenient and offenders can earn points for good behavior, (Z(s) 0), because there is an incentive to commit fewer crimes to accumulate more points and avoid being caught (Braithwaite, 2022). In the third case, where the sanction scheme is moderate, (Z(s) = 0), because there is an incentive to commit the same number of crimes under a strict or lenient sanction scheme, but there is an incentive to avoid getting caught under a moderate sanction scheme.
Variable Rate of Exogen Detection p
A variable exogen detection rate p is a function that determines the likelihood of an agent detecting a change in the value of a variable from one period to the next. The greater the value of p, the more difficult it is for an agent to detect a change in the variable’s value. The exogen detection rate p is determined by the endogenous punishment level p and the variable x. Exogen detection in this context refers to detecting changes in endogenous variables that are not caused by the agent. In a model with endogenous punishment, the exogen detection rate p is used to determine the equilibrium level of crime q. The level of crime q at which criminal behavior is maximized is known as the equilibrium level of crime. The exogen detection rate p is determined by the endogenous punishment level p and the variable x.
When p equals 1, the equilibrium level of crime q is determined. There is no incentive for criminals to commit crimes in this case, and criminal behavior is at its most basic. If p is greater than one, criminals have an incentive to commit crimes in order to avoid detection. The greater the value of p, the more difficult it is for agents to detect changes in x. In a model with endogenous punishment, the exogen detection rate p can be used to determine the equilibrium level of crime q. The level of crime q at which criminal behavior is maximized is known as the equilibrium level of crime (Barragan, 2022). Another way to look at this model is to add another variable y that controls how much information the public has about crime. If y is greater than zero, the public is well-informed about crime, and vice versa. When y is greater than zero, agents have a more difficult time detecting changes in x. When y equals zero, the equilibrium level of crime q is the level at which criminal behavior is maximized.
When y = 0, the equilibrium level of crime, q, is determined when p = 1. There is no incentive for criminals to commit crimes in this case, and criminal behavior will be maximized. When y equals one, the equilibrium level of crime q is determined by setting p equal to the value of x. In this case, agents have a very easy time detecting changes in x, so criminals have little incentive to commit crimes. When y = 0, the equilibrium level of crime q is determined when p = the value of y. Because agents have a difficult time detecting changes in x in this case, criminals have an incentive to commit crimes.
The Research Project’s Objectives
The goal of the research will be to determine how the equilibrium level of crime changes when the detection rate for criminals changes. This will be accomplished by investigating how x and y change in response to varying levels of p. Furthermore, the project will investigate how the equilibrium level of crime changes as the criminal population changes. Understanding these relationships allows people to make better decisions about how to reduce crime.
Program of Research
1. Determine how the equilibrium level of crime changes as criminal detection rates change.
2. Identify how the equilibrium level of crime shifts as the criminal population shifts. 3. Recognize how x and y change in response to varying levels of p.
4. Recognize how the equilibrium level of crime shifts as the criminal population shifts.
5. Create a model to explain these relationships.
6. Put the model to the test with real-world data.
7. Apply the research findings to improve crime-reduction strategies.
Schedule
The research will be organized chronologically, beginning with a conductive qualitative data analysis and progressing to data analysis using statistical models. The model will be tested using data from real-world scenarios in the final phase of the research project. This timeline is subject to change based on the research project’s findings.
Data conductive qualitative analysis:
This phase will include conducting interviews with criminology experts to better understand how x and y change in response to different levels of p. This data will be used to create a model that will explain how crime rates change as a result of various factors.
Statistical model data analysis:
This phase will involve the use of statistical models to analyze data from the previous phase’s interviews. This data will be used to create a model that will explain how crime rates change as a result of various factors.
Putting the model to the test with real-world data:
This phase will involve putting the model developed in the previous two phases to the test using real-world data. This data will be used to improve crime-prevention strategies.
References
M. Barragan (2022). Policing and Punishing Illegal Gun Behavior in Los Angeles: An Examination of Jail Detainee Experiences with Gun Law Enforcement 1170-1187 in Social Problems.
J. Braithwaite (2022). Maximum Accountability with the Strictest Punishment. 47, The Journal of Corporation Law.
A. Endres and B. Rundshagen (2012). Penalties that escalate: a supergame strategy Economic Governance, 13(1), pp. 29-49.
T. J. Miceli (2013). Why are escalating penalties for repeat offenders so difficult to explain? 587-604 in Journal of Institutional and Theoretical Economics: JITE.
M. C. Mungan (2014). A behavioral justification for punishment escalation schemes. 37, 189-197, International Review of Law and Economics.