AI-Assisted Simulations to Explore Responsibility and Escape from Responsibility ()
1. Introduction
Responsibility refers to the duty to perform tasks and fulfill roles within social, organizational, and institutional contexts. It specifies who holds which role, what that role entails, and what must be done to ensure success. Core features include reliability, commitment, accountability, and considerateness, which together make responsibility essential for the functioning of human systems. Research traditions in religion, education, law, philosophy, and organizational studies have tended to frame responsibility—whether individual or Corporate Social Responsibility (CSR)—as a primarily moral quality that must be cultivated and reinforced through education, incentives, regulation, and sanctions (Vogelmann, 2020).
Yet this view raises a paradox: if human beings are naturally capable of fulfilling their duties, why must responsibility be externally imposed? An alternative perspective is focused on responsibility of role incumbents (Negru & Dolfsma, 2022; Cane, 2016; Hart, 1968; Auhagen & Bierhoff, 2001; Allen et al., 2018; Christensen, 2019). Accordingly, responsibility is best understood not only as a moral obligation but as a practice contingent on resources. Like living systems that require inputs to adapt and respond, individuals and organizations need tangible, temporal, and intangible resources to meet their responsibilities. When these resources are lacking, responsibility falters.
Moreover, beyond failure within roles lies a different phenomenon: escape from responsibility, the deliberate avoidance of role-taking (Christensen, 2019; Fromm, 1941). Unlike irresponsibility, which implies breakdown within a role, escape concerns refusal to assume the role in the first place.
These issues had been discussed by the author in a recent paper (Bar-Haim, 2025), using facet theory analysis and three explorative studies. The conclusion from these studies is that responsibility is less about moral exhortation and more about creating the conditions that allow individuals, organizations, and societies to assume and fulfill their roles. Recognizing this shift is key to understanding both the fulfillment and the escape of responsibility in the modern world.
In discussing shortcomings and suggestions for further research in the above paper, I complete the necessary practice. However, actual “further research” is seldom pursued due to limited time and resources. Simulating plausible scenarios for subsequent studies in this field can support the preparation and planning of future research projects.
Three scenarios are reported:
1) Replicating a study on perceived responsibility by using an enhanced facet theory-based questionnaire and a simulated dataset (noting that the simulated data does not yield real empirical results).
2) Gathering a real-world dataset on escaping responsibility from multiple global statistical databases, incorporating an additional behavioral indicator.
3) Generating a facet theory-based survey questionnaire on escaping responsibility with AI and producing a corresponding simulated dataset, with the primary focus on the questionnaire design rather than the fictitious data.
2. The Semantic Space of the Concept Responsibility
Verbal Mode
Facet A: Responsibility essence is (low…high): 1) Reliability; 2) Commitment, liability; 3) Ability to decide and act according to assigned duties; 4) Ability to accept responsibility for failures (accountability); 5) Considerateness.
Facet B: Responsibility object is: 1) Individual self; 2) People, social entities; 3) Things; 4) Relationships.
Facet C: Responsibility requires (little…much) of:
C1: 1) Tangible resources; 2) Time; 3) Intangible resources.
OR:
C2: 1) Physical resources; 2) Knowledge & technology; 3) Financial resources; 4) Help from others; 5) Time; 6) Autonomy; 7) Attentiveness; 8) Motivation; 9) Diligence. 10) Resilience; 11) Management competence.
Graphical Mode
In the mapping sentence above, four elements have been added or changed:
Facet A (Essence of Responsibility): The element of considerateness was added. While the emotional aspect is not strictly part of formal role duties, it involves accepted norms; ignoring or violating these within a role partnership may be viewed as irresponsibility.
Facet B (Objects of Responsibility): The Individual (self) has been included but not surveyed empirically this time.
Facet C (Resources to Fulfill Responsibility): Autonomy has been added as a necessary resource for attaining role duties. Element 4 in facet C2, get help from others, is a generic resource. The former was skilled workforce, which is more appropriate to specific organizational context.
2.1. Replicating Study on Perceived Responsibility
The task is to replicate by simulation the empirical study that its hypothesis model is as follows.
2.2. Procedure and Results for Replicating Studies on Perceived Responsibility
2.2.1. Generating Simulated Data According to the Improved Questionnaire
A simulated sample of 400 respondents across six countries (including Israel) was generated according to improved facet approached questionnaire, using Gemini (see Appendix 1). The model utilized empirical marginal frequencies and correlations from Bar-Haim (2025), integrated with a normal copula.1
The resulting simulated data exhibited higher correlations than those observed in the empirical studies, leading to severe collinearity, as evidenced by high Variance Inflation Factors (VIF) among the predictors. This is a recognized occurrence in non-real-world data.2 Compared to real-world data, simulations are often displaying artificially low variance and limited generalizability (Koul, Duran, & Hernandez-Boussard, 2025). However, for exploratory studies where VIF is less critical than in medical or engineering fields, statistical solutions are possible. One approach that preserves the original data while minimizing VIF is to factor-analyze the collinear predictors and separate them orthogonally. The predictor variables in our study are 11 resources required to attain responsibility. Table 1 below displays the results of a two-factor orthogonal analysis performed on the C2 facet (Resources). Factor scores were calculated for each factor to ensure independence between the variables: Factor 1: Tangible Resources, Factor 2: Intangible Resources.
Table 1. Factor analysis of 11 resources defined in facet C2.
C2 facet: Resources |
Factor* |
1 |
2 |
d. physical resources |
0.804 |
0.568 |
e. knowledge and technological resources |
0.837 |
0.512 |
f. financial resources |
0.798 |
0.561 |
g. help from others |
0.747 |
0.588 |
h. time |
0.832 |
0.522 |
i. autonomy |
0.521 |
0.847 |
j. attentiveness |
0.807 |
0.570 |
k. motivation |
0.521 |
0.844 |
l. diligence |
0.639 |
0.733 |
m. resilience |
0.623 |
0.756 |
n. managerial competencies |
0.703 |
0.663 |
Variance |
52% |
44% |
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. |
*Bold = loadings above 0.7.
2.2.2. Results I: Responsibility Essence (Figure 1)
Path analysis with estimation method of Unweighted least squares, using AMOS 28, is employed to test the model.
The following represent possible, rather than real, results:
Note: The full model included Gender and Age. However, they over constrained the optimization and removed. Usually, this happens when those factors (along with education) are highly correlated, but that isn’t the case here.
Figure 1. Path analysis on responsibility essence: Reliability, Commitment, Accountability & Considerateness.
Intangible resources index (Factor 2) has the most powerful impact. Regardless of exogeneous demographics, if an individual perceives high sufficiency of intangible resources, s/he perceives responsibility essence (Reliability, Commitment, Accountability and Considerateness) exceptionally high (path coefficients > 4.0). This result replicates the findings in the real-world study (Bar-Haim, 2025).
All the path coefficients from the Tangible resources index (Factor 1) to the elements of responsibility essence are low around zero. This result corresponds with the real data of Bar-Haim (2025).
Exogeneous variable Income has considerable direct positive path coefficient to all essence elements (≥1.00).
Exogeneous Family Status variable shows that moving from Single to Married status through In-relations has a direct negative paths to the essence of responsibility - specifically Accountability and Reliability (path coefficients > -.7).
Education, as an exogeneous variable, has the highest direct positive path to Accountability and Considerateness (3.7), but direct high positive paths to Reliability and Commitment as well.
2.2.3. Results II: Responsibility Object (Figure 2)
Figure 2. Path analysis on responsibility objects: People, Things & Relationships.
The following represent possible, rather than real, results:
Intangible Resources: The Intangible Resources Index (Factor 2) exerts the strongest positive influence across all objects of perceived responsibility (People, Things, and Relationships). If an individual perceives high sufficiency of intangible resources, s/he perceives responsibility toward People, Things and Relationships exceptionally high (path coefficients > 2.0)
Tangible Resources: Contrary to previous findings (Bar-Haim, 2025), the Tangible Resource Index (Factor 1) also demonstrates a significant positive impact in this model.
Exogeneous Influence: Among the exogenous variables, Education and Income are the most influential factors driving perceived responsibility toward the three objects.
Gender: Gender displays moderate positive weights (ranging from 0.45 to 0.59), suggesting that women report slightly higher levels of perceived responsibility toward these objects than men.
Age: Age exhibits a negative correlation; younger participants report a higher sense of responsibility toward People, Things, and Relationships than their older counterparts.
Marital Status: Interestingly, as status shifts from Single to In-relations and Married, direct perceived responsibility toward People (−0.85) and Relationships (−0.85) significantly decreases.
2.2.4. Interim Summary
Alignment with the Model: If the results from the simulated replication of the perceived responsibility studies were replicated empirically, they would support the model’s main premises.
Demographic Differences and Relevance: The differences observed in the simulated demographics—if proven real—likely stem from the simulation being more detailed than the original empirical studies. Consequently, interpreting this specific segment may have limited standalone value, serving primarily as a reference for future empirical research utilizing identical demographic parameters.
3. Replicating Study on Escape from Responsibility
3.1. The Semantic Space for Escape from Responsibility
In the mapping sentence below, Facet B (Object of Escape) has been revised following a reassessment of behavioral indicators. The original Facet B contained four elements—Oneself, Relationships, People/Social Entities, and Things—all of which were initially adapted from the Responsibility concept mapping sentence. However, because the semantic space of escape from responsibility differs when expressed through behavioral indices, this improved version provides a more accurate conceptual fit.
Verbal Mode
Facet A: Elements of escape (low…high) 1) From commitment, liability; 2) From action/decision; 3) From accountability; From considerateness.
Facet B: Objects of escape: 1) Economic; 2) Social; 3) Relationships; 4) Bonding.
Facet C: Forms of escape: 1) Doing; 2) Attitudes.
Graphical Mode
3.2. Procedure and Results for Replicating Study on Escape from Responsibility
In the empirical study on Escape from Responsibility, five social, economic, and political indicators of escape were originally drawn from the OECD “Society at a Glance” series (2024). For this study, Gemini expanded the dataset to include six indicators gathered across 40 countries from various UN, OECD, and other global sources.
The retrieved 6 indicators of Escape from Responsibility are:
Fertility Rate (lower=escape).
Marriage rate (lower=escape).
Labor Force Participation Rate (lower=escape).
Not in Employment, Education or Training (NEET).
Community Non-Participation.
Political Non-Participation.
Results III: Escape from Responsibility (Figure 3)
Figure 3. Multidimensional scaling (ALSCAL) on indices escape from responsibility.
While Smallest Space Analysis (SSA) effectively uncovered structural patterns in the author’s original empirical study, it failed to provide adequate differentiation in the current, real-world AI dataset. For this specific dataset, SPSS Multidimensional Scaling (ALSCAL) produces better results. While Smallest Space Analysis (SSA) effectively uncovered structural patterns in the author’s original empirical study, it failed to provide adequate differentiation in the current, real-world AI dataset. For this specific dataset, SPSS Multidimensional Scaling (ALSCAL) produces better results.
The layout reveals a distinct structural pattern that is highly consistent between both datasets, defined by two primary dimensions:
Dimension 1 (Facet B: Objects of Escape): This axis differentiates the systemic scale of escape. It separates macro-level economic and social escape from micro-level relationship and bonding escape.
Dimension 2 (Facet A: Elements of Escape): This axis maps the behavioral profile of the escape. It distinguishes between an escape from commitment versus an escape from action.
4. AI Survey Questionnaire on Escape from Responsibility, and a Simulated Dataset as Complimentary Dimension to Behavioral Measures
To demonstrate the power of AI-assisted simulation, this section introduces a proposed research tool designed for future empirical research, serving as a “pilot” for a real-world study. This serves as a feasibility test for using simulation tools in questionnaire construction.
Procedure and Results
AI has been asked to generate: (a) an escape from responsibility questionnaire according to the above mapping sentence (Table 2), and (b) a realistic, anonymity representative sample for 150 Israeli respondents. The demographic distributions are based on publicly available Israeli data (e.g., Israeli Central Bureau of Statistics). 20 item questionnaire has been generated.
It has simulated also the expected social desirability bias (SDB). Due to the nature of the questionnaire, which asks about socially undesirable behaviors, SDB correction is critical to the expected higher or lower scores (more or less agreement) than in a standard survey. However, by this correction, SDB will be partially, but not completely, mitigated.3
Table 2. Item analysis of escape from responsibility questionnaire (the full questionnaire in Appendix 2).
Question |
Simulated mean score |
Question description |
Q1 |
3.53 |
Escape from Commitment: Options open |
Q2 |
3.59 |
Escape from Commitment: Look for easy way out |
Q3 |
3.47 |
Escape from Commitment: Prefer not to be tied down |
Q4 |
3.09 |
Escape from Commitment: Comfortable breaking promise (High Social Desirability Bias expected) |
Q5 |
3.55 |
Escape from Commitment: Following through feels heavy |
Q6 |
3.85 |
Escape from Required Action: Put off until last moment |
Q7 |
3.99 |
Escape from Required Action: Wait for someone else to start |
Q8 |
3.91 |
Escape from Required Action: Ignore requests for help |
Q9 |
3.12 |
Escape from Required Action: Pretend I didn’t notice (High Social Desirability Bias expected) |
Q10 |
3.31 |
Escape from Required Action: Justified in doing minimum |
Q11 |
3.07 |
Escape from Accountability: Blame external factors (High Social Desirability Bias expected) |
Q12 |
3.39 |
Escape from Accountability: Downplay role in negative outcomes |
Q13 |
3.04 |
Escape from Accountability: Difficult to admit I was wrong (Highest Social Desirability Bias expected) |
Q14 |
3.59 |
Escape from Accountability: Avoid formal justification |
Q15 |
3.45 |
Escape from Accountability: Stay “under the radar” |
Q16 |
3.86 |
Escape from Considerateness: Exhausting to constantly worry |
Q17 |
3.59 |
Escape from Considerateness: Rarely help people not important to me |
Q18 |
3.53 |
Escape from Considerateness: Delegate emotional support |
Q19 |
3.84 |
Escape from Considerateness: People are too sensitive |
Q20 |
4.02 |
Escape from Considerateness: Prioritize own comfort (Highest overall mean) |
Overall Mean: The mean scores are above the neutral point (3), ranging from 3.2 to 3.8 on a Likert scale of 1 to 5.
High SDB Items: Questions that imply direct moral wrongdoing (e.g., Q4: Breaking a promise, Q13: Admitting I was wrong) still have lower means, as even high anonymity will not fully eliminate the internal reluctance to admit to serious moral failings.
Low SDB Items: Questions related to procrastination or self-care (e.g., Q6: Putting off tasks, Q16: Exhausted by worrying about others, Q20: Prioritizing own needs) have higher means, as these are more common and socially accepted flaws.
5. Summary
Traditional research constraints, namely limited time, and resources, mean that the “further research” suggested in academic papers is rarely executed. This study proposes using AI-assisted simulations (via Gemini) to create plausible scenarios, questionnaires, and datasets based on Facet Theory. This approach serves as a pilot mechanism to help researchers plan and prepare for future real-world empirical projects.
Three Simulated Scenarios & Key Findings
Scenario 1: Replicating Perceived Responsibility
Procedure: Simulated a sample of 400 respondents across six countries using an improved questionnaire framework.
Findings: While the simulated data showed higher-than-normal multicollinearity, factor analysis successfully separated the variables into Tangible and Intangible resources. Path analysis revealed that—assuming these patterns hold true in an empirical dataset—Intangible Resources (e.g., autonomy, motivation, resilience) exert the strongest positive impact on an individual’s sense of responsibility.
Scenario 2: Replicating “Escape from Responsibility”
Procedure: Gathering a dataset tracking six behavioral and socioeconomic indicators across 40 countries (including low fertility rates, low marriage rates, low labor force participation, high NEET status, and low community/political participation).
Findings: Using SPSS Multidimensional Scaling (ALSCAL), the AI-generated dataset successfully mapped behaviors along two axes: Scale of Escape (Macro vs. Micro) and Profile of Escape (Commitment vs. Action). This structural layout proved intrinsically valid, matching patterns found in real-world empirical data.
Scenario 3: Designing an Escape Questionnaire (Micro Level)
Procedure: Generated a 20-item survey tool and simulated a sample of 150 Israeli respondents, factoring in expected Social Desirability Bias (SDB).
Findings: The simulation produced a robust survey tool. Items tracking mild or socially acceptable flaws (e.g., prioritizing personal comfort, procrastination) yielded high agreement scores. Conversely, items tracking explicit moral failures (e.g., breaking promises, refusing to admit wrongdoing) retained lower scores despite the anonymity framing.
6. Conclusions and Lessons
Value of AI Simulations: AI simulations are highly valuable for pilot testing, questionnaire development, and research planning; however, they cannot substitute for real empirical studies.
Methodological Challenges: The primary challenge in generating and analyzing AI-simulated survey data is variable redundancy and multicollinearity, which limit the ability of simulations to serve as accurate proxies for reality. While statistical techniques can mitigate these effects, researchers must remain cautious regarding the discrepancies between simulated datasets and real-world data.
Future Research: Currently, the “missing link” in empirical studies on the “escape from responsibility” is the actual perceived behavior of such escape. Deploying the simulated questionnaire developed in this study into a real-world empirical project can serve the next step to bridge this gap.
Appendix 1. Questionnaire on the Perceived Responsibility
Please, circle the suitable answer you most agree with (there is no right or wrong in this section).
If responsibility toward people (employees, clients, suppliers, colleagues, neighbors, friends, family) were the following properties, how would you describe your responsibility toward people?
Reliability: 1- High 2- Medium 3- Low
Commitment: 1- High 2- Medium 3- Low
Ability to accept responsibility for failures: 1- High 2- Medium 3- Low
Considerateness: 1- High 2- Medium 3- Low
If responsibility toward something (product, service, tools, facilities, the environment) were the following properties, how would you describe your responsibility toward things?
Reliability: 1- High 2- Medium 3- Low
Commitment: 1- High 2- Medium 3- Low
Ability to accept responsibility for failures: 1- High 2- Medium 3- Low
Considerateness (mainly, toward service):1- High 2- Medium 3- Low
If responsibility toward relationships (proper conducting, considerateness) is the following properties, how would you describe your responsibility toward relationships?
Reliability: 1- High 2- Medium 3- Low
Commitment: 1- High 2- Medium 3- Low
Ability to accept responsibility for failures: 1- High 2- Medium 3- Low
Considerateness: 1- High 2- Medium 3- Low
Let us move now to several questions about the resources which are available to you to fulfill your responsibilities. Resources can be physical, financial, knowledge and technology, time, AND less tangible such as autonomy, attentiveness, motivation, diligence, resilience, and management competencies.
Please, circle the suitable answer you most agree with (there is no right or wrong in this section).
Appendix 2. Survey on Escape from Responsibility
This survey aims to understand the ways individuals navigate, avoid, or escape various forms of responsibility in their lives. Please indicate the extent to which you agree or disagree with the following statements, using the scale below:
The questionnaire ratings are based on a 1-5 Likert scale: 1 = Strongly Disagree 5 = Strongly Agree.
(Focuses on avoiding or withdrawing from personal promises, agreements, and long-term duties.)
No. |
Statement |
Your Rating (1-5) |
1. |
I often find ways to keep my options open instead of making a firm commitment. |
|
2. |
When a commitment becomes difficult, I am likely to look for an easy way out. |
|
3. |
I prefer not to be tied down by long-term plans or obligations to others. |
|
4. |
I am comfortable with breaking a promise if a better opportunity arises. |
|
5. |
Following through commitments feels like a heavy burden. |
|
(Focuses on procrastination, inaction, or delegation when specific, necessary tasks must be performed.)
No. |
Statement |
Your Rating (1-5) |
6. |
I frequently put off necessary chores or tasks until the last possible moment. |
|
7. |
I will often wait for someone else to start a required task before I get involved. |
|
8. |
I tend to ignore requests for help with basic tasks if they don’t directly benefit me. |
|
9. |
If a task is unpleasant, I will pretend I didn’t notice it needed to be done. |
|
10. |
I feel justified in doing the minimum required for tasks I find boring or unpleasant. |
|
(Focuses on avoiding blame, consequences, and owning up to personal failures or errors.)
No. |
Statement |
Your Rating (1-5) |
11. |
When a plan fails, I look for external factors or other people to blame. |
|
12. |
I tend to downplay my role in negative outcomes to avoid criticism. |
|
13. |
It is difficult for me to admit I was wrong even when the evidence is clear. |
|
14. |
I avoid situations where I know I will have to give a formal justification for my actions. |
|
15. |
I try to stay “under the radar” to avoid being held responsible for group errors. |
|
(Focuses on avoiding empathy, concern for others’ feelings, and necessary emotional labor.)
No. |
Statement |
Your Rating (1-5) |
16. |
I find it exhausting to constantly worry about how my actions affect others. |
|
17. |
I rarely go out of my way to help people who are not immediately important to me. |
|
18. |
When someone needs emotional support, I often try to delegate the task to someone else. |
|
19. |
I believe people are too sensitive and should not take my words/actions personally. |
|
20. |
I prioritize my own comfort and needs above considering the inconvenience to others. |
|
NOTES
1A copula is a statistical function used to describe the structure between multiple variables by joining (coupling) their individual, univariate marginal distributions into a single multivariate cumulative distribution function. Here Gaussian (normal) is used, Other commonly used copulas are Clayton, Gumbel, and Frank.
2Non-real-world data includes simulated data, modeled after real-world scenarios like those in this study, and synthetic data, generated for specific objectives in fields such as medicine or engineering.
3On extensive discussions about SDB in simulated and synthetic data (called sometimes Silicon sampling), see: Argyle, et al, 2023; Chapala, Mironov & Deng, 2025.