1 Introduction

The origins of parole in the United States can be traced back to the reformatories of the late 1800s. In these facilities, inmates could earn their way to an early release through good behavior, hard work, and participation in programming (Abadinsky 2011). From its inception, parole decision making was largely based upon a clinical model, meaning the decision was based on the expertise and experience of the decision makers (Rhine et al. 2016). Due to criticisms regarding the inherent subjectivity and arbitrariness of such decisions, parole decision making has undergone major reforms, most notably since the 1970s (Abadinsky 2011). Standardized assessment tools and legislation creating guidelines for decision-making, though not without their problems, have resulted in a more transparent and objective parole decision-making process.

Today, parole structures and adjudication vary widely among the states, and a number of studies have examined how parole decisions are made. Indeed, as these decisions are necessarily made with limited information, involve the freedom of the potential parolees, and potentially impact the safety of the public, it is essential to fully understand and optimize the parole process. However, while researchers have studied the offender and case factors used to make parole decisions, there is a dearth of research regarding how the individual and collective characteristics of parole board members can influence decision-making. Personality is most notably a factor that influences decision-making. The current study will simulate parole board members and their personalities in order to explore how personality might impact these critical decisions.

2 Background

Parole is the early supervised release from a term of incarceration and includes a set of agreed upon conditions (U.S. Department of Justice (USDOJ) 2015). These conditions include standard conditions, such as regular meetings with a parole officer and refraining from associating with felons, and may also include specific conditions, such as random drug tests and treatment for drug offenders or internet restrictions for cybercriminals. The purposes of parole are threefold: to assist the offender with reintegration into society, to protect society from the offender, and to prevent needless imprisonment of those who are unlikely to commit future crime (USDOJ 2015).

In the United States, there were approximately 870,500 individuals on parole at yearend 2015, a 1.5% increase from the previous year (Kaeble and Bonczar 2017). Nationally, parole boards grant parole in roughly 43% of cases; this, however, varies greatly by state, with a low of 0% of cases being paroled (Illinois) and a high of 87% of cases being paroled (Arkansas and Nebraska) (Alper et al. 2016). The proportion of parolees who either completed their supervision or were granted early release (termed the exit rate) increased in 2015 to 54 per 100 parolees (Kaeble and Bonczar 2017). Parolees are mostly male (87%) and are most likely under supervision for a violent (32%) or drug (31%) crime. In addition, the parole population is overwhelmingly either White, non-Hispanic (44%), Black (38%), or Hispanic (16%) (Kaeble and Bonczar 2017). While these demographic and offense statistics do not match those of the general U.S. populations, they are somewhat representative of those of the prison population from which this group largely comes.

2.1 Parole Boards

Parole granting decisions happen in two main ways. One is termed mandatory parole, and is calculated based upon the amount of time served, incorporating good timeFootnote 1, as set in state statute. The other is termed discretionary parole, and is granted by parole boards. The current study will focus on discretionary parole.

Parole Board Composition and Structure.

As stated earlier, parole overall varies widely from state to state; this is true also for discretionary parole specifically. State parole boards range in size from a low of 3 members (Alabama) to a high of 17 members (New York). However, the average size of parole boards is about 7 members, with 30 state parole boards having between 5 and 8 members, inclusive. Some parole boards are only responsible for making decisions to release offenders or revoke their parole while others are also responsible for the supervision of offenders in the community (Abadinsky 2011). In addition, parole board members are typically appointed by the governor. However, in a few states, appointment may be by the Board of Corrections, the state attorney general, or a combination of these (Abadinsky 2011; Kinnevy and Caplan 2008).

Finally, parole boards vary with regard to their qualifications, which is a source of much criticism. Only a few states actually have any specific professional qualifications (Abadinsky 2011; Paparozzi and Caplan 2009). The joining of selection by appointment with no specific qualifications is said to insert politics into parole board selection and also to result in board members that are not qualified for the job.

Eligibility and Case Type.

Another way that parole varies by state is through when offenders become eligible for parole. Many states have requirements about how much of the sentence the offender must have served before he or she becomes eligible for parole; this ranges from one-third to 85% (Alarid and Del Carmen 2011). These minimums may become even lower when good time is included; some states may also give credit for time served in jail prior to the sentence (Alarid and Del Carmen 2011). If an offender is denied parole, states vary as to when that offender will come up for parole again.

In addition, some states may require that the offender have a place to live and a job already established before they can be released (Abadinsky 2011; USDOJ 2015). Another way parole differs by state is in the types of cases that are eligible for parole. For example, some states have abolished parole for violent offenders or for serious repeat offenders. Other states only consider misdemeanors for discretionary parole. Still others only consider those convicted prior to a certain date; these states are typically moving to a mandatory parole model.

Parole Board Adjudication.

The process by which decisions are made by parole boards is also determined by state. Boards may be divided and assigned to different parts of the state (Alarid and Del Carmen 2011). Parole boards vary with regard to how many make a quorum as well as how many have to meet for violent versus non-violent offenses. For example, certain types of serious offenses, such as sex offenses, may require a full board review (Alarid and Del Carmen 2011). Most states require either a majority or a unanimous decision; however, there are a few which specify a different number of votes. Moreover, some states have in-person hearings to inform the parole decision, while others have paper reviews where the offender is not present. See Table 1 for a breakdown of parole composition and adjudication.

Table 1. Board sizes and adjudication

Parole boards will have access to the offender’s case file which contains case and background information, and quite probably a risk assessment. In fact, the vast majority of parole authorities (88%) report using some type of risk assessment in their decision-making (Kinnevy and Caplan 2008). This risk assessment calculates the offender’s probability of parole failure, meaning reoffense or violating a condition of their parole. Typically in the in-person hearings, the offender, the prosecutor, law enforcement, the direct victim will be invited to make a statement, either oral or written (Alarid and Del Carmen 2011). Some states may allow the offender’s family to be present. Using information from the case file and the hearing, the parole board will make their decision.

2.2 Decision-Making

A decision is a choice between alternatives (Houston 1999). In order to choose between alternatives, a decision maker applies decision rules to information (Stojkovic et al. 2015). Decision rules are criteria used to process information in order to make a choice and come in a variety of forms (Stojkovic et al. 2015). They can be quantitative in nature, such as a calculated risk score, and they can be clinical in nature, such as judgments based upon experience; individuals may even be unaware of their decision rules, such as a person who is unconsciously biased against minorities (Stojkovic et al. 2015). In the case of parole decision-making, decision rules include parole guidelines created by the legislature or the correctional authority, judgements based upon training and education, standardized risk assessments, and, most importantly for this study, rules from personality traits. The information used by parole members for decision-making include the offender’s case file as well as any oral or written statements made, as outlined above.

According to March and Simon’s theory of bounded rationality, in order for decisions to be fully rational, decision-makers must have two things: perfect information and the appropriate amount of time to fully process and make the decision (March and Simon 1958). In real life, however, information is never perfect and decisions typically have a deadline. As such, March and Simon posited that decisions are not made rationally, but on the basis of bounded rationality. Due to a lack of information and time, decision made through bounded rationality will be acceptable, rather than optimal. “Satisficing” is a term used to describe attaining acceptable (versus ideal) results based upon incomplete information in a limited amount of time (Stojkovic et al. 2015).

For parole decision-making, the information used will definitely be incomplete. For example, information may be missing from an offender’s case file, victims and family members may not make a statement and some institutional behaviors may have gone undetected. Indeed, the offenders themselves are incentivized to hide information about themselves when they make their statement at the hearing, a prime example being an offender faking remorse or a commitment to change. In addition, risk assessments used by parole boards are far from perfect predictors. In fact, one of the most common risk assessments, the LSI-R (Kinnevy and Caplan 2008) was found in one study to have a 30% false positive error rate (Hemphill and Hare 2004). Another popular risk assessment, the COMPAS, was found to have a 70% accuracy in predicting general rearrests, but did poorly in predicting future violent behavior (Zhang et al. 2014). Other scholars have also outlined the predictive and validity issues with risk assessments (Desmarais et al. 2016).

Moreover, parole caseloads and the time available to hold hearings will limit the amount of time parole boards have to process information from each case. The average parole board caseload in 2006 was approximately 35 cases for each working day (Kinnevy and Caplan 2008). Therefore, due to imperfect information and limited processing time, parole board members must “satisfice” when they decide whether or not to grant parole. Because the reality of parole decision situations makes bounded rationality a necessity, improving the amount of information available and fully understanding the decision rules used (such as those derived from personality traits) are critical to optimizing parole decision-making.

Factors Impacting Parole Decision-Making.

A number of studies have been conducted to determine which factors are most important in determining the parole decision. These factors are usually offender and case factors. In a 2008 national survey, parole board chairpersons reported the nature and severity of the current offense, the offender’s prior record and risk assessment, the offender’s institutional disciplinary record and program participation, previous parole adjustment, and victim input as the most important factors in the parole decision (Ruhland et al. 2016). In addition, the parole chairpersons overwhelmingly (87%) stated that they either agreed or strongly agreed that risk assessments were essential in parole decision making (Ruhland et al. 2016, p. 8).

Other studies have found similar results, concluding that parole authorities consider offense seriousness, institutional misconduct, and parole readiness (Huebner and Bynum 2006), institutional behavior and risk of reoffense (Carroll et al. 1982), and risk of future crime (Carroll 1978) as most important. While victims do not often take advantage of the opportunity to make a statement, there is evidence that when they do, these statements are related to a lower likelihood of the board granting parole (Bernat et al. 1994; McLeod 1989; Morgan and Smith 2005; Parsonage et al. 1994; Smith et al. 1997). In addition, the offender’s age, history of drug and alcohol use, percentage of the sentence that has been served, and parole hearing demeanor are often considered as well (Abadinsky 2011; Kinnevy and Caplan 2008; Ruhland et al. 2016). In the current study, the simulated parole board members will use the following factors for their decision-making: severity of the offense, prior record, institutional record, rehabilitative program completion, risk of re-offense, age of the offender, victim statements, offender remorse, and abuse of alcohol and drugs. These factors make up the information upon which the decision rules of the different personalities will operate.

Personality and Decision-Making.

Because personality is said to impact choices, values, and reactions (Myers 1962), it seems logical that personality will act upon the different parole factors to influence parole decisions. Indeed, one of the few studies that focused on parole board members rather than offender and crime characteristics found that parole decision making was an outcome of interactions within the parole board (Conley and Zimmerman 1982).

Probably the most well-known and widely used personality test is the Myers-Briggs Typology Indicator (MBTI) (Furnham 2017). The theory behind the MBTI is that differences in the way people take in information (perception) and make conclusions about that information (judgement) relate to the variations in values, reactions, choices, and behaviors (Myers 1962). Under the MBTI, there are 4 basic preferences that make up a person’s personality: between extroversion and introversion (E or I), between sensing or intuition (S or N), between thinking or feeling (T or F), and between judging or perceiving (J or P). (Myers 1962; The Myers & Briggs Foundation 2017).

The preference between extroversion and introversion refers to whether an individual prefers to focus on the outer world or their inner world. The preference to focus on basic information or interpret and add meaning is the sensing/intuition preference. The preference between an initial focus on logic and consistency or people and circumstances is the thinking/feeling preference. Finally, the judging/perceiving preference is between having things decided or being open to new information (Myers 1962; The Myers & Briggs Foundation 2017). Thus, a person who prefers to focus on their inner world, to focus on basic information, to focus on logic and consistency, and who prefers to have things decided would be said to have an ISTJ personality. The simulated parole board members have been coded so that they have various types of the 16 MBTI personalities that will function as decision rules for them.

3 Methodology

The current study simulates parole board members and their personalities in order to explore how personality might impact parole adjudication. There are 16 different sets of code that are used to represent the different personality types. A number of different trials were performed to represent different parole situations because, as stated above, there exists wide variation in parole board makeup and adjudication.

3.1 Coding

The simulated parole board members will utilize bias-based reasoning (BBR) in their decision-making process. BBR is a proprietary mathematical method for automating implementation of a belief-accrual approach to expert problem solving (Hancock 2012). It enjoys the same advantages human experts derive from this approach; in particular, it supports automated learning, conclusion justification, confidence estimation, and natural means for handling both non-monotonicity and uncertainty. Dempster-Shafer Reasoning is an earlier attempt to implement belief-accrual reasoning, but suffers some well-known defects (Lotfi paradox, constant updating of parameters, monotonic, no explicit means for uncertainty) (Boyen and Koller 1998). BBR overcomes these.

Pose a problem for a human expert in their domain, and you will find, even given no evidence, that they have an a priori collection of beliefs about the correct conclusion. For example, a mechanic arriving at the repair shop on Tuesday morning already holds certain beliefs about the car waiting in Bay 3 before she knows anything about it. As she examines the car, she will update her prior beliefs, accruing “bias” for and against certain explanations for the vehicle’s problem. At the end of her initial analysis, there will be some favored (belief = large) conclusions, which she will test, and so accrue more belief and disbelief. Without running decision trees, applying Bayes’ Theorem, or using margin maximizing hyperplanes, she will ultimately adopt the conclusion she most believes is true. It is this “preponderance of the evidence” approach that best describes how human experts actually reason, is fully in line with March and Simon’s theory of Bounded Rationality, and it is this approach that is modeled in BBR.

For simplicity and definiteness, the reasoning problem will be described here as the use of evidence to select one or more possible conclusions from a closed, finite list that has been specified a priori (the “Classifier Problem”). Expert reasoning is based upon facts (colloquially, “interpretations of the collected data”). Facts function both as indicators and contra-indicators for conclusions. Positive facts are those that increase our beliefs in certain conclusions. Negative facts are probably best understood as being exculpatory: they impose constraints upon the space of conclusions, militating against those unlikely to be correct. Facts are salient to the extent that they increase belief in the “truth”, and/or increase “disbelief” in untruth (Delmater and Hancock 2001).

A rule is an operator that uses facts to update beliefs by applying biases. In software, rules are often represented as structured constructs such as IF-THEN-ELSE, CASE, or SWITCH statements. We use the IF-THEN-ELSE in what follows.

Rules consist of an antecedent and a multi-part body. The antecedent evaluates a BOOLEAN expression; depending upon the truth-value of the antecedent, different parts of the rule body are executed. The following is a notional example of a rule. It tells us qualitatively how an expert might alter her beliefs about an unknown animal should she determine whether or not it is a land-dwelling omnivore:

figure a

If we have an INCREASE BELIEF function, and a DECREASE BELIEF function (“aggregation functions”, called AGG below), many such rules can be efficiently implemented in a looping structure:

In a data store:


truth-value of predicate j applied to fact Fi

bias(kj, 1):

belief to accrue in conclusion k when predicate j true

bias(kj, 2):

disbelief to accrue in conclusion k when predicate j is true

bias(kj, 3):

belief to accrue in conclusion k when predicate j false

bias(kj, 4):

disbelief to accrue in conclusion k when predicate j is false

Multiple rule execution in a loop:

figure b

This creates a vector B of beliefs (b(1), b(2), …, b(K)) for each of the conclusions 1, 2, …, K, and a vector D of disbeliefs (d(1), d(2), …, d(K)) for each of the conclusions 1, 2, …, K. These must now be adjudicated for a final decision.

Clearly, the inferential power here is not in the rule structure, but in the “knowledge” held numerically in the biases. As is typical with heuristic reasoners, BBR allows the complete separation of knowledge from the inferencing process (Friedman et al. 1998). This means that the structure can be retrained, even repurposed to another problem domain, by modifying only data; the inference engine need not be changed. An additional benefit of this separability is that the engine can be maintained openly apart from sensitive data.

Summarizing (thinking again in terms of the Classifier Problem): When a positive belief heuristic fires, it accrues a bias β > 0 that a certain class is the correct answer; when a negative heuristic fires, it accrues a bias δ > 0 that a certain class is the correct answer. The combined positive and negative biases for an answer constitute that answer’s belief.

After applying a set of rules to a collection of facts, beliefs and disbeliefs will have been accrued for each possible conclusion (classification decision). This ordered list of beliefs is a belief vector. The final decision is made by examining this vector of beliefs, for example, by selecting the class having the largest belief-disbelief difference.

BBR can also incorporate variation in decision-making that is a result of uncertainty, termed bias-variability (Cover and Thomas 2001). Human decision-making is variable, and an individual, given the same input, may make two different decisions in two different situations. The introduction of some randomness, or bias-variability, into the decision-making can simulate the non-determinism inherent in human decision-making. Thus, each factor has some measure of variation in its code, using a subroutine; in the current study, the bias-variability is small enough that it only impacts decisions that would be considered “borderline” cases.

3.2 Parole Board Members

In the parole board situation, parole board members use a variety of factors (evidence) to make the decision to grant or not grant parole. In the current experiment, the simulated parole board members will review evidence regarding severity of the offense, prior record, institutional record, rehabilitative program completion, risk of re-offense, age of the offender, victim statements, offender remorse, and abuse of alcohol and drugs. Severity of the offense, prior record, institutional behavior, and risk of re-offense were considered the strongest factors, as per prior research.

As an example, consider severity of the offense. If an offender has been incarcerated for a relatively minor offense, then, all else being equal, that would increase the board members’ belief that parole should be granted. Conversely, if an offender has been incarcerated for a severe offense, say murder, this would increase the parole board members’ disbelief that parole should be granted. All else being equal, there is probably a low level of uncertainty about this decision. Conversely, while demeanor is a factor that parole board members consider, this is not only a more subjective factor than severity of offense (a legal concept) and considered less predictive of later behavior, but remorse and respect are easily faked by an offender. As such, the importance of this factor will be lower and the uncertainty for this factor is going to be higher.

Once all of the factors are considered, each parole board member will have a belief score between −1 and +1 as to whether parole should be granted. The threshold for parole was set at 0.4; scores higher than this resulted in granting parole, lower resulted in denying parole. In this way, each parole member has a vote based upon their belief score; these votes can be combined in whatever way the method of adjudication for the current trial requires (i.e. majority, unanimous, etc.) to get the ultimate decision.


In addition, it is theorized that different personality types will vary with regard to how much different factors influence them. The personality variations are incorporated into the code by changing the amount of belief (☐s) and disbelief (☐s) each factor causes for each personality type. Moreover, the influence of uncertainty is also altered for each personality type. These personality variations resulted in 16 different sets of rules for parole decision making.

The following represent some examples of the thought process behind the coding of parole board members’ responses to the parole decision factors. These examples are far from exhaustive.

Severity of Offense, Prior Record, and Risk of Reoffense.

While most, if not all, individuals are likely to consider these as important (regardless of personality type), different types may react to these differently. For instance, ‘feelers’, ‘intuitives’ and ‘perceivers’ might be slightly more open to considering mitigating factors when it comes to severity of offense and prior record. In addition, it is possible that risk of reoffense is generally less important to ‘feelers’ compared to more emotionally salient factors like the offense itself and victim statements. ‘Sensors’ might be more likely to take the risk assessment score as is, whereas ‘intuitives’ might be more likely to extrapolate from the score, but might also be more likely to question the accuracy of the risk assessment.

Victim Statements and Offender Remorse.

Victim statements and offender remorse might not be as important to board members as the severity of offense; the exception might be ‘feelers’ (as stated above) who place high value on personal and emotional factors, whereas ‘thinkers’ might be more inclined to put the more objective factors first.

Abuse of Substances.

‘Intuitives’ might have less of a problem with mild abuse, but see heavy abuse without treatment as indicative of future problems.

Variability in Decision-Making.

When it comes to making choices, variability within individuals is possible. ‘Perceivers’ on average are going to exhibit higher variability in their responses than ‘judgers’, and, to a lesser degree, ‘intuitives’ more than ‘sensors’.

3.3 Offenders

For this experiment, 500 offenders were generated with different scores on each of the factors to be considered by the parole board members. Severity of offense ranged from 1–5, prior record ranged from 1–6, institutional behavior 0–5, and risk assessment from 1–10. For all of these, lower scores are better. Additionally, program completion and victim statements were dichotomous as either present or not. Use of substances ranged from 0–2, with 0 being no history and 2 being a severe user. Demeanor was an abstract scale ranging from 0–1, with 1 being a great demeanor. Finally, age ranged from 17–75, with the age makeup of the 500 offenders closely mirroring that of the overall U.S. prison population.

3.4 Experiments

Experiment 1: MBTI Type and the Parole Decision.

For this experiment, each MBTI type made parole decisions as individuals. 1,000 trials were run in which each type made parole decisions for the 500 simulated offenders.

Experiment 2: Career-Based Parole Boards.

One of the ways MBTI is used is to assist with career guidance. Different personality types seem fitted to certain types of careers. For this experiment, the personality types were divided into two groups and seven personalities were randomly selected from each group (reflecting the average parole board size):



Group 1 consists of personality types whose most recommended careers fall into criminal justice, psychiatry, counseling, rehabilitation, and social work (Schaubhut and Thompson 2008), careers which seem fitting for parole board members. Indeed, for those states that have requirements for parole board members, having experience in one of these fields is a common standard.

For the experiment, 1 Million simulations were run using the two groups. There were approximately 200 Group 1 Boards and 200 Group 2 boards and each board processed all 500 candidates 5 times. To adjudicate, the scores of each individual member (from summing the betas and deltas described above) were averaged to get an overall board score.

4 Results

4.1 Experiment 1

The results of the first experiment can be seen in Table 2. As shown, there was obvious variation in the parole decision-making of the MBTI types, with the ISFJ/ESFJ types granting parole about half the time while the INTJ/ENTJ types denied parole in almost all the cases. The ISFPs and ESFPs are most in line with the national parole grant rate as reported by Alper et al. (2016).

Table 2. Parole decisions by MBTI type

Looking at the differences in percentages granted/denied, there are clear gaps between sets of MBTI types; these are marked by lines in the table. The decisions seem to largely cluster into 4 groups: the NTs, the STs, the NFs, and the SFs. As such, it appears the strongest personality factors in parole decision-making are the middle two preferences: sensing/intuition and thinking/feeling. This is illustrated in Fig. 1.

Fig. 1.
figure 1

Histograms of aggregate scores of NT/SF decisions (Color figure online)

What is also interesting is that, while the ‘thinkers’ and ‘feelers’ are clearly divided on parole, the ‘sensors’ and ‘intuitives’ are not. This distribution suggests that the combination of the two middle preferences is important. This makes sense, given that the two middle letters indicate how one gathers information and makes decisions.

What is perhaps most interesting is the judging/perceiving preference. Indeed, this preference seems to become more important in the parole decision as one moves down the table. For the SF personality types, the judging/perceiving preference seems more influential in their decision making; in contrast, the judging/perceiving preference seems to have little impact with the NTs. Furthermore, the Judging/Perceiving preference seems to have a different relationship with the NT personality type, as with the NTs, ‘judgers’ are least likely to grant parole, while with all the other types, they are most likely to grant parole.

4.2 Experiment 2

The results of experiment 2 can be found in Table 3.

Table 3. Parole decisions by career groups

As can be seen, Group 1, the career group that, on the surface, seems ideal for parole board membership, was unlikely to grant parole. This is not surprising in light of the results of experiment 1, given that 3 of the 4 NT types were in Group 1 while 3 of the 4 SF types were in Group 2. These results can also be seen in Fig. 2.

Fig. 2.
figure 2

Histogram of career group decisions (Color figure online)

In addition, Table 4 shows the correlation between the MBTI makeup of the boards and the overall board score. The strongest positive correlations are for ESFPs and ESFJs, indicating that the more individuals with these personality types, the higher the scores for the board, thus being more likely to reach the threshold to grant parole. In contrast, the strongest negative correlations were for ISTJs and ISTPs; boards with more of these individuals had lower scores and thus were less likely to grant parole. The weakest correlations were for the ENTPs and the ISFJs (which are, interestingly, exact opposites).

Table 4. Correlations between score and board makeup

Table 5 shows the correlation between the overall board scores and the offender factors. The factors most strongly correlated to the board’s overall score were the completion of a rehabilitative program, the severity of the offense, and the risk assessment. This is in line with what parole board chairs have said are the most important factors in the parole decision (Ruhland et al. 2016). The weakest correlation was for the use of substances.

Table 5. Correlations between board score and offender factor

5 Discussion

The results of these experiments have suggested the importance of personality type to the parole decision-making process. As such, further research in this area is warranted, given the implications of parole decisions for social welfare, justice, and community safety.

Based upon experiment 1, there are clearly some MBTI types that are more likely to grant parole than others. Again, while the ‘thinkers’ and ‘feelers’ seem divided on parole, the ‘sensors’ and ‘intuitives’ are not. This relationship does make sense: ‘thinkers’ tend to put more weight on objective principles, hard facts, and logic, while ‘feelers’ put more weight on the personal or human aspect. It is not unexpected that the ‘thinkers’ would be “harsher” with regard to parole decisions and the ‘feelers’ more “lenient.” In contrast, ‘sensing’ versus ‘intuition’ is more about how one orders and processes information, so it seems logical this would be less influential in the decision outcome and more related to how the individual arrived at the decision.

Moreover, the results showed that the “parole” career group was much less likely to grant parole than the other group. While there is some disagreement on career recommendations for MBTI types, three of the four NT types were in the career group. Due to these individuals’ emphasis on logic, theory, and rationality, it is not surprising to find them in this group, as they are likely more rule and law oriented. Along the same lines, it makes sense that this group would be “harsher” with regard to parole. An implication of this is that drawing parole board members from a wider variety of career fields may be worth consideration. The greater assortment of careers could potentially diversify the personality makeup of the board, perhaps resulting in lower denial rates, if indeed that is found to be desirable.

The current study found a relationship between MBTI type and likelihood to grant or deny parole; this finding is value neutral. Consequently, one important topic to research in the future would be the connection between board member personality type and parole board effectiveness. One way this could be measured would be the accuracy of the parole decision-whether the individual who is paroled reoffends. It is possible that some MBTI types, because of the way they filter and prioritize different types of information, may be better able to discern an offender’s potential for success upon release. Indeed, Sanchez (2011) studied MBTI types and their ability to detect lies; she hypothesized that ENTPs would be most accurate due to their focus on others (extroversion), their ability to see patterns of deceit cues (related to intuition), their focus on logic and analysis (thinking), and their less rigid thinking patterns (perceiving). Ultimately her results supported her hypothesis. The ability to detect lies would certainly be beneficial when interviewing a parole candidate.

This examination of effectiveness could also be extended to looking at the interplay between personalities on parole boards. There may indeed be an optimum mix of MBTI types on a parole board that yield more accurate or efficient decisions. For example, when it comes to ‘feeling’ versus ‘thinking’ dimension, the MBTI dimension correlates with the Big 5 agreeability dimension, where MBTI ‘feelers’ are far more agreeable than their ‘thinker’ counterparts (Furnham 1996). This may make ‘feelers’ generally more inclined to go with the group consensus, whereas ‘thinkers’ are generally more inclined to ignore it and stick to their own opinions. However, both ‘feelers’ and ‘thinkers’ have their judgements to which they want to stick, and a ‘feeler’ can cling just as strongly to their values as a thinker to their reasons, as long as they feel strongly enough about it. In that case, a ‘feeler’ might be similarly unwilling to compromise. A ‘thinker’ on the other hand might think that it’s more reasonable to compromise when they are not that certain about their own opinion. Similarly, a ‘perceiver’ might be generally more inclined to be laid back and open to adjust to the group consensus than a ‘judger’, who is generally more likely to stick to their principles. However, ‘perceivers’ have their principles, too, and can become very rigid about them. Thus, the ability to reach group consensus may be dependent not just on their attitude towards group consensus itself, but also on the strength of their conviction that they made the right decision. At the very least, the findings of this study indicate the need for more scrutiny regarding who is chosen for parole boards and stronger standards for selection.

It would also be valuable to examine in depth the interplay between the ‘judging’ preference and the parole decision. Experiment 2 showed an interesting relationship between the ‘judging’ preference and the NT personality type. The NT types tend to be logical, objective, analytical, and impersonal (The Myers & Briggs Foundation 2018). It is possible the more task-oriented, structured ‘judging’ preference amplifies the NT characteristics in ways that it doesn’t with other types. This interaction could potentially result in seemingly “harsher” decisions.

Finally, it would be interesting to examine the relationship between personality and other criminal justice decisions, including judges, attorneys, and correctional officers. Indeed, while parole boards generally have limited time for each decision, police officers must often make split second decisions, so examining personality types in policing is also important. On the other side of the system, environmental conditions, genetics, and socialization may interact with personality type to influence the decision to commit crime.

6 Conclusion

Personality is a critical factor in decision-making, and this fact seems no different for parole boards. The factors influencing parole decisions are important as these decisions influence rehabilitation of offenders, the safety of the public, and the legitimacy of the criminal justice system.