The task is to replicate by simulation the empirical study that its hypothesis model is as follows.

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 [4], 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 ([10]). 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.

*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 ([4]).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 [4].Exogeneous variable Income has considerable direct positive path coefficient to all essence elements (≥1.00).ExogeneousFamily Statusvariable 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)

Path analysis with estimation method of Unweighted least squares, using AMOS 28, is employed to test the model.

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 ([4]), 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.

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

In the empirical study on Escape from Responsibility, five social, economic, and political indicators of escape were originally drawn from the [12] “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.

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).

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.

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.