1. Introduction
Management today functions largely as a reactionary force in organizations, supporting the propagation of the dominant capitalist ideology and the maintenance of the socio-political status quo (Braverman, 1974; Burnham, 2021; Jackson & Heath, 2025b; Marcuse, 2013; Willmott, 1997). In terms of internal operations, organizations can be understood as sites of power, coercion, and subjugation (Fleming & Spicer, 2014; Jackson, 2022; Mintzberg, 1983). As previously explained, “work, and by extension all those who are engaged in its execution, will end up being either revolutionary or reactionary” (Jackson & Heath, 2025a, para. 1), and “if managers were the true leaders of workers they could…function as a driving force for revolutionary change rather than reactionary enforcers of capitalist ideology” (Jackson & Heath, 2025b: p. 2726). Moving from reactionary to revolutionary aims within organizations requires both an increase in management awareness and engagement in a revolutionary project with enhanced solidarity among workers. Neither will be easy to achieve. Management is incentivized to comply with organizational preferences and pursue individual advancement over collective improvement (Durand et al., 2019; Jackson & Heath, 2025b; Kim, 2025). Mobilizing the workforce is complicated further by differences in worker perceptions of power operating within organizations (Abou Elnaga & Imran, 2014; Jackson, 2025; Jackson et al., 2022), and risks associated with unionization (Braymen, 2024; Logan, 2021; Plata, 2025). Overcoming these constraints requires increased class consciousness for mobilization and action (Braverman, 1974; Debord, 2014; Meyer, 2008). Once class consciousness is formed, it can be directed to transcending the constraints of capitalist ideology (Engles, 2020).
The dominant ideology is the ideology of the dominant class (Althusser, 2014, 2020; Marx & Engles, 2023). As Althusser (2014) explained, the “dominant ideology = ideology of the dominant class” and the purpose of capitalist ideology was to “guarantee the conditions for the exploitation of the exploited classes by the dominant classes, above all the reproduction of the relations of production in which this exploitation takes place” (p. 93). Common aspects of the capitalist ideology include the role of meritocracy in success and advancement (Derber et al., 2015; Walo, 2023), the sovereignty of private property rights (Rossi & Argenton, 2021; Wisman, 2020), and that poverty results from individual failures rather than systemic inequalities (Harriss-White, 2006; Patnaik, 2012). Primarily, capitalist ideology focuses on the individual over the collective (Kennedy, 2017; Turner, 1988). Focusing on the individual, and making economic failure personal rather than systemic outcome, both constrain the formation of the class consciousness needed for worker solidarity and deflect attention away from the role capitalism plays in subjugating and exploiting workers. This is facilitated and enhanced by the pro-business, anti-labor, framing of news adopted by corporate media (Kollmeyer, 2004; Martin, 2015). Under capitalist ideology, management enacts corporate policy to maximize profits for businesses or accomplish missions and goals for nonprofits (Friedman, 2007; Maier et al., 2016; Sanders & McClellan, 2014), and preclude the formation of worker solidarity focused on collective improvement. As explained by Althusser (2014), “capitalist relations of production are relations of capitalist exploitation” (p. 29). Those wanting to escape this subjugation can focus on either reform or revolution.
The question of revolution or reform was central to the work of Luxemburg (2024) and Lenin (2011, 2013). As indicated by Luxemburg, “reform and revolution are not different methods of historic development that can be picked out at pleasure…reform and revolution are different factors in the development of class society,” in which every reform “is the product of a revolution” (p. 78). The essential thing to note is that real reform requires a revolution. An echo of this view can be found in Lenin when he noted reforms are “the cheapest and most advantageous concessions to make” (p. 62) for those in power, but “the fundamental economic interests of the proletariat can be satisfied only be a political revolution” (p. 47). Still, it would be prudent to pursue reform over revolution, if reform were effective. However, as Jackson and Heath (2025b) explained, “while proponents of stakeholder capitalism and co-determination models argue that these frameworks offer more inclusive and equitable approaches to corporate governance…such business reforms frequently fail to dismantle the entrenched structural and ideological barriers that perpetuate worker subjugation” (p. 2723). Those ideological barriers are consequential. Debord (2014) effectively linked ideology with revolution (Jackson & Heath, 2024). Capitalist ideology obfuscates the class struggle residing at its core. Althusser (2020) explained, “the revolutionary power of the masses comes precisely from the class struggle” (p. 82), and that “class struggle is not an individual struggle, but an organized mass struggle for the conquest and revolutionary transformation of state power and social relations” (emphasis retained, p. 86). To determine if it is possible for a management revolution to contribute to this class struggle, a simulation was needed.
Whereas simulations in general, and agent-based modeling more specifically, have been previously applied to aspects of business including operations (Aghababaei & Koliou, 2023; Yang et al., 2023), organizational behavior (Pires et al., 2024; Tang & Liu, 2023) and consumer behavior (Abbasi Siar et al., 2022; Wang et al., 2018), and to aspects of revolutions including civil unrest and protests (Adhikari et al., 2023; Dacrema & Benati, 2020), rebellion and insurgency (Oloba, 2020; van der Zwet et al., 2023), and regime change (Klein et al., 2019; Lemos, 2018), this approach has yet to be applied to understanding the propensity for a worker-focused, management revolution while operating under the dominant, capitalist ideology. Such an approach is beneficial as it enables the exploration of system dynamics under various model assumptions and operational conditions (Schwaninger & Grösser, 2018; Sterman, 2018; Subramanian et al., 2018). Simulations are especially beneficial as they provide analytic insights when alternative methods are too costly, time-consuming, or would be unethical to enact (Gilbert et al., 2018; Lamé & Dixon-Woods, 2020). For this study, an agent-based simulation model was built in RStudio to determine if a management revolution is possible. Triangular distributions were used to model each of the seven input variables. Proxy parameters were identified and substantiated through research, and modeling interpretations of all parameters were made to favor the generation of reactionary over revolutionary managers.
Key aspects of the enactment and execution of management operating under the dominant, capitalist ideology have been presented, along with a rationale for a management revolution over reform. Elements of simulations and agent-based modeling were introduced. These aspects were developed more fully in the following method overview (Section 2). After which, the results were presented and interpretations offered (Section 3). Based on the simulation results, a discussion of implications was developed (Section 4), followed by a conclusion (Section 5).
2. Method
Simulations are computational models that abstract and simplify the operations of real-world systems (Chebotar et al., 2019; Law, 2022; Tulli, 2024), allowing researchers to control and manipulate variables to understand complex system outcomes (Jalali et al., 2019; Lukosch et al., 2018; Yin et al., 2021). Such models enable one to explore system dynamics under various model assumptions and operational conditions (Schwaninger & Grösser, 2018; Sterman, 2018; Subramanian et al., 2018), providing insight that is often unattainable through alternative approaches due to cost, time, or ethical considerations (Gilbert et al., 2018; Lamé & Dixon-Woods, 2020). Regarding the form of simulation used in this study, Epstein (1999) explained that agent-based modeling is, “a powerful tool in the analysis of spatially distributed systems of heterogeneous autonomous actors with bounded information,” and that the approach is “well suited to the study of connectionist phenomena in social science” (p. 56). This study of the propensity of managers to adopt a reactionary or revolutionary ideological stance within organizations is situated in the domain of connectionist phenomena in social science, comprised of heterogeneous, autonomous actors.
It is important to note at the beginning that this simulation model was neither attempting to model what has happened historically, nor what has occurred recently, within organizations operating under capitalist ideology. Rather, this simulation model attempted to determine if a management revolution is possible, given reasonable assumptions related to anticipated levels of support and resistance. Key to that determination was the observation of whether the simulated proportion of radicalized managers exceeded a minimum critical mass threshold viewed as reasonably necessary to sustain a management revolution, if that were ever to become the aim of a dedicated, core subgroup of managers. The approach taken holds implications for the determination of a reasonable critical mass threshold zone and for model validation. Each of these aspects was addressed more fully in this section. Prior to addressing those concerns, it is essential to understand the model in general terms and provide a detailed summary of the key inputs and parameters used.
This management radicalization simulation was built in RStudio using the dplyr (Wickham et al., 2023, version 1.1.4), and triangle packages (Carnell, 2022, version 1.0). The data visualizations for this study were developed using the ggplot2 (Wickham et al., 2025, version 3.5.2) package. To facilitate transparency and replication of the model, the code used for the simulation is included in Appendix A, and the code for the data visualizations is included in Appendix B. In addition to the provided code, a user-friendly, input adjustable and editable, version of the simulation was built in Anthropic’s AI Claude, and is available for free (https://tinyurl.com/mtyjz6xz). The developed simulation model contained seven key input parameters. The distribution selection and input parameters for each of those seven inputs are explained in detail following a brief, general summary of the input parameters and distribution selection.
The simulation model was based on seven key parameters: a) the initial proportion of revolutionary managers within an organization, b) the susceptibility of currently reactionary managers to becoming revolutionary due to peer influence (i.e., becoming radicalized based on peer interaction with revolutionary managers within the organization), c) the susceptibility of currently reactionary managers maintaining their reactionary commitment due to capitalist pressure from within the system (i.e., either from higher echelon executives within the organization, or from media, market dynamics, or the influence of the dominant social ideology), d) the susceptibility of currently reactionary managers becoming revolutionary due to pressure from workers (i.e., becoming radicalized based on solidarity with those from a lower echelon within the organization), e) internal revolutionary group solidarity boosting commitment to revolutionary praxis once a manager has become radicalized, f) reactionary pressure for a revolutionary manger to revert back to a reactionary manager, and g) the probability that a revolutionary manager is identified as being revolutionary by the elites of the system and subsequently removed from one’s managerial position within organization. For each of the risk distributions, a triangular distribution was selected using research-based parameter ranges. For most, if not all, of the variables, the data would likely be more appropriately modeled using a uniform distribution, as there was little to suggest that any one value was more likely than another. However, triangular distributions were used to favor reactionary over revolutionary values. In terms of modeling, the most likely value for each triangular distribution was selected to favor reactionary over revolutionary outcomes. Given the intent of this simulation to determine if an effective management revolution is possible, it was considered appropriate to make the affirmative outcome more difficult to achieve, ceteris paribus. This will become clearer through the presentation of the key input variables.
The initial proportion of revolutionary managers was an initializing variable that had a positive effect on the number of revolutionary managers in the model. It was modeled using a triangular distribution with a minimum and most likely value of 0%, and a maximum value of 6%. This means that the most likely scenario is that an organization starts the simulation with zero revolutionary managers among its ranks, but it could contain up to 6%. As previously indicated, the distributions were modeled to favor reactionary over revolutionary outcomes. The minimum and most likely values of 0% reflect a starting position in which no managers hold revolutionary views. The maximum value of 6% was based on research conducted by the Pew Research Center. According to their research, 6% of U.S. adults say they view socialism very positively (Pew Research Center, 2022). This was considered a reasonable proxy for a maximum proportion of revolutionary managers, as individuals holding these positions come from the underlying population. However, due to selection biases in employment and advancement, it is possible that those in management positions do not reflect the same political allegiances as those of the underlying population (Amos et al., 2021; Viinikainen et al., 2020). In general, it is reasonable to assume that those holding such positions tend to be more conservative (i.e., reactionary) than the underlying population (Fos et al., 2022; Hersh & Shah, 2025). However, given that this value is being used to substantiate the maximum value possible within the simulation, these concerns, while acknowledged, are not considered limiting.
The peer influence variable promoted conversion to revolutionary management through peer pressure. The Asch (1951, 1955) conformity studies explored “the conditions of independence and submission to group pressure” (1951, p. 177). In his earlier work, Asch (1951) found “that a disturbance of the unanimity of the majority markedly increased the independence of the critical subjects. The frequency of pro-majority errors dropped to 10.4 percent of the total number of estimates in variation” (p. 186). One might infer that having a person bolster one’s stake in an unpopular position will result in the person holding firm to what one thinks 89.6% of the time (100% − 10.4% = 89.6%). In a subsequent study, Asch (1955) found that “the presence of a supporting partner depleted the majority of much of its power. Its pressure on the dissenting individual was reduced to one forth” (p. 34). The results of Asch have been largely replicated (Franzen & Mader, 2023) or partially replicated (Mori & Arai, 2010) in subsequent studies. So, the presence of a similarly minded individual in this case would allow the individual to hold firm with an unpopular opinion 75% of the time (100% − 25% = 75%). Using the information from the Asch (1951, 1955) conformity studies, peer influence was modeled with a triangular distribution with a minimum and most likely value of 75%, and a maximum value of 89%.
The capitalist pressure variable captures systemic pressure under the dominant capitalist ideology and promotes reversion to reactionary management. The lasting effects of exposure to propaganda are pronounced. Research conducted by Edelson et al. (2011) on “socially induced memory errors” found that “participants exhibited a strong tendency to conform to erroneous recollections of the group, producing both long-lasting and temporary errors, even when their initial memory was strong and accurate” (p. 108). They found that participants conformed to the majority opinion 68.3% of the time and that even after social influence was removed, participants maintained erroneous answers 40.8% of the time. Using this research as the foundation for parameter selection, the input variable for the pressure to conform to the dominant capitalist ideology was modeled using a triangular distribution with a minimum value of 40%, and a maximum and most likely value of 70%.
The radicalized worker pressure variable captured subordinate pressure and promoted conversion to revolutionary management from below. Research from DeVore et al. (2021) found that “86% of employees feel people at their workplace are not heard fairly or equally” (p. 4). Based on that value, it was assumed that the maximum efficacy of radicalized worker pressure on management was 14% (100% − 86% = 14%). This variable had the weakest level of influence. A triangular distribution with a minimum and most likely value of 0% and a maximum value of 14% was used.
Research by Keenan et al. (2022) reported a link between higher levels of employee belonging and a 50% decrease in employee turnover. According to research by Pierce (2025), the average turnover rate is between 12% and 20%. So, if there is a 50% reduction to a 12% to 20% turnover rate, this amounts to an improvement of between 6% and 10% in retention. Since this is a variable that supports revolutionary management, the 6% value was treated as the minimum and most likely value, and the 10% was treated as the maximum value. The solidary-enhancing group cohesion variable promotes conversion and stability within revolutionary manager agents. A triangular distribution was used with a minimum and most likely value of 6% and a maximum value of 10%.
The reactionary pressure represented ideological drag and promoted a reversion to reactionary management under the capitalist ideology. This input was modeled as having a minimum value of 0% and a maximum and most likely value of 20%. The closest analogue to reverting to a previously held ideology was found to be the proportion of people who held a religious belief, disaffiliated with that faith, and then subsequently returned after a period. Research by Cranney et al. (2024) placed the reversion rate between 17.4% to 27%. For this study, these values were rounded up to between 18% for the minimum value and 30% for the maximum and most likely values, since that treatment favored reactionary over revolutionary management.
The last input variable was the detection and removal of revolutionary managers from the system, with their subsequent replacement with a new reactionary manager that could turn out to be either revolutionary or reactionary in subsequent time steps. This was a suppression variable that removed revolutionaries from the system. The ability to do this was constrained by an organization’s ability to identify a manager as a revolutionary rather than one conforming to the desired reactionary ideology. Research from Dyck et al. (2024) found that “a substantial amount of corporate fraud remains undetected” (p. 755). And that detection rate ranged between 29% and 52%. In the context of this study, from the view of the organizational elites and capitalists, revolutionary management could be considered a form of corporate fraud, and its identification rates might conform to those of other forms of organizational fraud. Based on this interpretation, and rounding the values up to benefit reactionary over revolutionary management, the identification and removal of revolutionary managers’ distribution was modeled as having a triangular distribution with a minimum value of 30% and a maximum and most likely value of 55%.
Given the details of these seven simulation input variables, it is useful to place them into a table to summarize the parameters and facilitate a comparative analysis. That information is contained in Table 1.
Table 1. Simulation input parameters summary.
Variable Title |
Min-Max; Most Likely Values |
Role Type |
Revolutionary Polarity |
initial_revolutionaries |
0%-6%; 0% |
Initialization |
NA |
peer_influence |
75%-89%; 75% |
Peer Pressure |
+ |
capitalist_pressure |
40%-70%; 70% |
Systemic Pressure |
- |
radicalized_worker_pressure |
0%-14%; 0% |
Lower Pressure |
+ |
solidarity |
6%-10%; 6% |
Peer Cohesion |
+ |
reactionary_pressure |
18%-30%; 30% |
Ideological Drag |
- |
removal_probability |
30%-55%; 55% |
Suppression |
- |
As indicated by the summary information contained in Table 1, after the number of initial revolutionaries was set in the simulation model, there was an equal number of positive variables (i.e., variables increasing the likelihood of a manager transitioning to the revolutionary state), and negative variables (i.e., variables increasing the likelihood of a manager conforming to the capitalist ideology, or reactionaries). The positive variables were peer_influence, radicalized_worker_pressure, and solidarity. The negative variables were capitalist_pressure, reactionary_pressure, and removal_probability. In terms of ranges and most-likely values, most of the variables can be seen to dramatically favor reactionary over revolutionary outcomes. Two of the variables, initial_revoluionaries and radicalized_worker_pressure had most-likely values of 0%. The only value that seemed questionable, in a relative sense, was that the most-likely peer_influence value (75%) was slightly higher than the capitalist_pressure value (70%). Given the rationales behind the input selections, these values were used in the model. However, this is likely the easiest point to critique. The input values were determined to be substantiated, balanced, and reasonable.
Agent radicalization scores were initialized by a random selection process within the simulation. The initialized agent radicalization score was selected from the defined triangular distribution with a most-likely value of 0% and a maximum value of 6%. At each time-step update, the agent radicalization score interacted mathematically with the parameters of the other simulation variables exerting either positive or negative pressure on the agent radicalization score, where peer influence, radicalized worker pressure, and solidarity exerted positive pressure, and capitalist pressure, reactionary pressure, and removal probability exerted negative pressure. A manager’s ideological state was updated through a probabilistic decision process driven by social and structural pressures combined with one’s individual radicalization level. In the case of a reactionary manager, the combined pressure to become revolutionary was calculated as the sum of weighted influences: radicalized worker pressure, solidarity, and peer influence, each multiplied by the proportion of revolutionary peers. This aggregate pressure was then added to the manager’s individual radicalization score to form a total revolutionary propensity value. A uniform random number between 0 and 1 was generated, and if that random number was less than the total propensity value, the manager switched to a revolutionary ideology; otherwise, the manager remained reactionary. Conversely, a revolutionary manager faced a probability of reverting to reactionary based on the sum of reactionary and capitalist pressures, scaled by one minus their radicalization score. If a generated random number fell below this reversion chance, the manager reverted; if not, the manager maintained a revolutionary stance. This simulated framework modeled ideological shifts as stochastic outcomes resulting from the interplay of peer effects, systemic pressures, and intrinsic radicalization. The proportion of revolutionary managers was then compared to the minimum critical mass threshold zone to determine if a management revolution is possible.
The minimum critical mass zone for management revolution was established based on research on socio-political revolutions. The work of Chenoweth and Stephen (2011) suggested that nonviolent movements with active participation from at least 3.5% of the population were consistently successful. However, research from Dacrema (2020) looking at protests in Arab countries found cases with participation rates as high as 18.4% that did not achieve their revolutionary aims. This suggests that 3.5% might function as a lower threshold value, but that the upper threshold would need to be a value exceeding 18.4%. Research from Centola et al. (2018) found that, “minority groups can initiate social change dynamics in the emergence of new social conventions” (p. 1116), and that “the critical mass prediction is not exact” but ranges from 20% to 30%. Based on these findings, the minimum critical mass zone for a successful management revolution was bounded between 3.5% and 30%. Whereas the critical-mass range (3.5% to 30%) used in this simulation is relatively wide, there is empirical evidence to support its underlying reasonableness if not its general robustness. The work of Chenoweth and Stephan, previously referenced, was based on their extensive, Nonviolent and Violent Campaigns and Outcomes (NAVCO) data set, which consists of “323 violent and nonviolent resistance campaigns between 1900 and 2006” (p. 6). Given the robustness of their findings, and the near, order of magnitude range from that value, the minimum critical mass zone of this study is considered at least plausible.
The agent-based simulation model ran in chunks of 10 simulations, aiming to reach a high degree of precision (±0.5%) in estimating the percentage of revolutionary managers at the end of the simulation (i.e., step = 50). The model required a minimum of 30 runs to ensure a sufficient sample size was achieved to establish that the variability estimates were meaningful. A maximum of 1,000 runs was set as a safety cap to prevent the simulation from running indefinitely if convergence to the desired confidence interval width failed to be achieved. Given the nature of the model and the contestability of model validation more generally (Heath & Jackson, 2013), no model validation beyond face validity was conducted. Whereas comparing model output consistency with a single historical case does not validate the model, it does, however, bolster the face validity of the simulation beyond expert judgement. Research by Chenoweth and Stephen (2011) found that the nonviolent campaign of the Philippine People Power Revolution, which occurred between 1983 and 1986, had an estimated level of participation of about 2,000,000 people (which accounted for about 4.2% of the population at that time). The case of the Philippine People Power Revolution suggests that a successful, nonviolent revolution can occur within the critical-mass range used within this model, and as such, provides a brief, case-based validation of the model.
The simulation was allowed to progress for 50 time-steps. Time steps have no inherent temporal meaning (Siegfried et al., 2009). During a time step, all agents updated their ideology based on pressures, peer influence, and their own radicalization level. Revolutionary agents could be removed and replaced with new reactionary managers. And the current proportions of reactionary and revolutionary managers were recorded. A value of 50 time-steps could indicate an interval of 50 seconds, minutes, hours, days, or years. In the real-world, the temporal meaning of a “time-step” is ambiguous, contestable, and consequential. Different mappings of a time-step (e.g., days, weeks, months) would almost assuredly influence the practical interpretation of the findings. The central question regarding the temporal meaning of a time-step, is how long it takes for an individual to be open to redefining one’s ideological commitments. At the individual level, this is idiosyncratic. Collectively, a period of days or weeks, is likely too short. Some research suggests that if people become open to reassessing their previously held positions, they do so after a couple of weeks or months (Festinger et al., 2008). Months starts to approximate a reasonable time-step for ideological shifts. If the interpretation of time-step extends to years, a period of 50 time-steps exceeds that of an entire career or perhaps a lifetime. Such an interpretation mitigates against revolutions. For these reasons, in practical terms, months appear to be the minimum, reasonable and practicable time-step duration. Consequently, a time-step in this simulation model could loosely be interpreted as being equivalent to a period of about a month, with 50 time-steps taking a little over four years. Placing the simulated time-steps into time is potentially beneficial for context, but unnecessary for the operation of the simulation model.
The method described the nature of simulation models, the intent of this study, the RStudio packages used, the rationale behind the input parameters, the functioning of the management ideology determinations, the minimum critical mass zone for successful management revolutions, and the operation parameters of the simulation. Based on the understanding of the simulation model, it is now possible to turn to the results of this study.
3. Simulation Results
The results of this simulation examining management ideology and revolutionary potential were assessed in three distinct parts. The first part was focused on the results covering simulation convergence to the desired confidence interval. The second aspect analyzed covered the comparative proportional analysis of reactionary to revolutionary management agents. The last aspect analyzed compared the simulated proportion of management agents holding a revolutionary ideology to the minimum critical mass threshold considered necessary for a successful revolution. Collectively, these results established the statistical significance of the simulated results and provided a basis for determining if a management revolution is possible.
The results of this simulation model converged to the 95% confidence interval target width of ±0.05%, after 810 iterations. The incremental conversion to the target value based on simulation runs is presented as Figure 1.
Figure 1. Convergence to 95% confidence interval target width of ±0.05%.
As indicated in Figure 1, the confidence interval width of this simulation decreased significantly as the number of iterations increased. This result was determined to be consistent with established simulation theory (Hoad et al., 2010; Law, 2024). The model was set to terminate iterating after the 95% confidence interval width target value of ±0.05% was achieved. As previously indicated, that level of precision occurred after 810 simulation iterations. Achieving the target value provided a statistically reliable estimate of the output variable (Flegal & Gong, 2015; Silva & Zhuang, 2022), which in the context of this study was the proportion of managers holding reactionary or revolutionary ideologies, which are presented in Table 2.
Table 2. Steady-state management ideology results.
Management Ideology |
Minimum |
Mean |
Maximum |
Standard
Deviation |
Reactionary |
46% |
68% |
86% |
7.24% |
Revolutionary |
14% |
32% |
54% |
7.24% |
As indicated in Table 2, 68% (SD = 7.24%) of the managers were found to hold a reactionary ideology whereas 32% (SD = 7.24%) held a revolutionary ideology. Because the model forced the management ideology into two oppositional categories, reactionary or revolutionary, it was expected that the summation of simulated mean would be 100%. This expectation was met. It should be noted that within this model, the SD values for reactionary and revolutionary managers were the same (7.24%). The fact that these values were the same was not unexpected since reactionary and revolutionary managers, as modeled in this simulation, were perfectly negatively correlated with each other (Aitchison, 1982; Chayes, 1960). Consequently, given the inherent linearity between these two elements, the calculated SD values should be the same, which they are in this case. The simulated results across the 50-step simulation are portrayed graphically in Figure 2.
Figure 2. Management ideology assessment. (Mean ± SD)
The simulated results in Figure 2 show that once the initial simulation position was randomly established, the proportion of revolutionary managers increased to its maximum mean value of ~35%, whereas the proportion of reactionary managers decreased to its minimum mean value of ~65%. The model results stabilized around time-step 10, exhibiting its stead-state, mean values of 68% reactionary and 32% revolutionary managers, as reported in Table 2. Over the simulation run, the minimum value for revolutionary managers was 14%, at the point when the maximum value for reactionary managers was 86%. Again, due to the nature of the proportional allocation, the average values sum to 100%, and the offsetting minimum and maximum values also sum to 100%. Based on these simulated results, it was possible to answer the central question of this research, as to whether a management revolution is possible. To do this, one is required to narrow one’s focus to the simulation results for revolutionary managers and compare those values to the minimum critical mass threshold values considered necessary for a successful management revolution. That assessment is presented in Figure 3.
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Figure 3. Revolutionary critical mass assessment. (Mean ± SD)
As described in the method section (Section 2), the minimum critical mass zone for successful management revolution extended from a lower threshold value of 3.5% to an upper threshold value of 30%. That area was denoted with a gray rectangle in Figure 3. The steady state, mean value for revolutionary managers (32%), and much of the uncertainty range exceeded the upper threshold value, suggesting that a successful management revolution is possible. However, there was a degree of overlap between the lower uncertainty range associated with revolutionary managers (denoted by pink shading) and the upper portion of the minimum critical mass zone for a successful management revolution (denoted by gray shading), suggesting that the chances for success are, at best, slightly ambiguous. If an actual attempt at a management revolution tended toward the lower end of solidarity, and the threshold required for success was more exacting than normal, the attempted revolution might be thwarted.
Given the nature of the simulation inputs, a case could be made that the use of uniform distributions would have been more appropriate than triangular distributions, as there was little, or no, a priori reason to assume a given value from among the identified ranges was any more likely than another. However, the decision was made to skew all the distributions toward selecting reactionary over revolutionary outcomes to make hitting the required critical mass as difficult as possible, under the modeling assumptions. A sensitivity analysis was conducted using uniform distributions to compare those results with those generated using triangular distributions. The results are presented in Table 3.
Table 3. Triangular uniform distribution sensitivity on revolutionary managers.
Distribution Type |
Minimum |
Mean |
Maximum |
Standard Deviation |
Triangular |
14% |
32% |
54% |
7.24% |
Uniform |
17% |
37% |
61% |
8.01% |
As indicated in Table 2, when the triangular distribution was used, 32% (SD = 7.24%) of the managers were found to hold a revolutionary ideology whereas 37% (SD = 8.01%) held a revolutionary ideology when the uniform distribution was used. These values are generally consistent with each other and conform to the expectation that the values generated using the triangular distributions are more disfavorable to the creation of revolutionary managers. The results of this sensitivity analysis are suggestive that the model results are neither artefacts nor unduly optimistic toward revolutionary outcomes.
The simulated results suggest that the model was able to generate statistically significant results at the desired level of precision. Those results suggest that reactionary managers (M = 68%) always exceed revolutionary managers (M = 32%) on average, with only the most extreme simulation results indicating a revolutionary management proportion of more than 50%. With such a level, it was considered possible for that level of revolutionary managers to succeed in a management revolution. Implications of these results, as well as limitations of this study, require further development and examination. An exploration of the major elements of concern is presented as a discussion (Section 4).
4. Discussion
The results of this study suggest that a management revolution is possible. However, getting to that conclusion, through the building of this simulation, required overcoming challenges both in terms of model development and results interpretation. That issues along these lines existed was not unique to this study, as simulated studies are constrained due to the inherent limitations of simplifying assumptions, data quality, and human bias in modeling (Robinson & Brooks, 2024; White et al., 2024; Williams et al., 2022). Whereas it is important to acknowledge the general limitations of simulations, there is benefit too from examining the specific limitations of any given simulation study. This discussion focused on two types of issues confronted in the development of this study: a) simulative, and b) ideological. The simulative aspects discussed were related to the modeling parameters used in the simulation model and the hypothetical nature of its focus. The ideological aspect discussed was focused on the lack of content associated with the management revolution. Discussing these aspects of the simulation and its results and implications acknowledges its limitations and points to potential future research.
To start, whereas a rationale was provided for each of the parameter values used in this simulation, all the modeling parameters were ultimately proxies. This is not an uncommon practice. As indicated by Stahlecker and Trenkler (1993), “applied work…frequently faces the problem of measuring unobservable or latent variables by proxies” (p. 707). The use of proxy parameters for variable modeling can potentially limit both model accuracy and the generalizability of the simulated results (Kallus et al., 2022; Seltzer, 2021). One central concern associated with the use of proxy parameters in this study was that it induced measurement error (Giannetti, 2021; Lang & Pearson-Merkowitz, 2022). Because the proxy parameters may only partially reflect the actual parameters of interest, the use of proxies could lead to inaccurate conclusions. Relatedly, there is the concern of weak correlation (Krasker & Pratt, 1986; Lunsford, 2015), if the proxy parameters used for variable modeling are only weakly related to the actual parameters of the variables of interest, the simulation may detect relationships that aren’t truly present, or miss real relationships that are. In terms of how the use of proxy parameters limits the generalizability of the simulation results, it is essential to acknowledge that the proxy parameters likely have different levels of relevance across populations and time (Luijken et al., 2019). Due to cultural variation, the application potential of proxy parameters can vary widely contextually, culturally, and temporally. Whereas the results of this study suggested that a management revolution is possible, theoretically, any attempt at an actual revolution should be based on a detailed analysis of the specific situation being confronted. As Lenin (2013) explained, “when you undertake to solve a concrete organisational problem surely you must take time and circumstance into consideration” (p. 136).
The simulation modeled a hypothetical future scenario in which a relatively small cadre of radicalized managers (M = 32%) attempt a management revolution against the dominant capitalist ideology operating within organizations and society. Whereas there is some minority support for socialism within the society (Pew Research Center, 2022), such support in the abstract does not necessarily translate into galvanized, revolutionary action within organizations. The model implicitly presupposed that something has sparked revolutionary interest within organizations. Research suggests that many people are dissatisfied with at least certain aspects of work. In fact, research conducted by the Pew Research Center (2024) found that “40% of workers express dissatisfaction with the amount of flexibility they have to work remotely” and 80% indicated that “their pay hasn’t kept up with increases in cost of living.” Research from Gallup, found that “employee engagement in the U.S. fell to its lowest level in a decade in 2024, with only 31% of employees engaged” (Harter, 2025). Whereas it is certainly the case that many are dissatisfied with their work, and that some view socialism as a viable alternative to the dominant, capitalist ideology operating socially, politically, and organizationally, it is a significant leap to conclude that such discontent will move to revolutionary fervor. A gap exists between thought and action (Moder, 2021; Verma, 2025). For those interested in enhancing worker solidarity and directing general discontentment toward specific, revolutionary aims, this gap will need to be bridged. The results of the simulation suggest that if such a focus emerged, dynamics within the system could reasonably coalesce in such a way that a critical mass of revolutionary managers could be achieved.
Lastly, the simulation results indicated that a unified minority of radicalized managers could achieve the theorized critical mass needed to effectively achieve revolution against reactionary forces operating under the dominant capitalist ideology. As modeled in the simulation, there was no specific content associated with that revolution. Whereas anarchy can potentially be about nothing other than nihilism (Franks & Wilson, 2018; Johnston, 1900), revolutions are about something (Brinton, 1938; Skocpol, 1979). While perhaps too reductionist, in broad strokes one could claim that the American Revolution was about colonial independence from British rule, the French Revolution was about dismantling the monarchy, aristocracy, and inequality, the Haitian Revolution was about ending slavery and French-colonial rule, and the Cuban Revolution was about removing U.S.-backed dictator Fulgencio Batista and establishing a socialist state. Galvanizing active revolutionary resistance requires an aim beyond simply venting one’s frustration (Tilly, 1978). This simulation was not focused on defining the aims of a management revolution, apart from it simply offering a worker-focused alternative to the situation produced under capitalist ideology. Again, those interested in changing the management praxis and rebalancing power between labor and capital will need to define the revolutionary aims. When that is done, it will help bridge the gap between discontent and action mentioned previously.
The limitations of this simulation study can be discussed in terms of simulative and ideological concerns. Due to the lack of available data related to organizational revolutions, the modeling parameters used in the simulation model were constrained to proxy inputs. Additionally, the simulation was hypothetical in focus, if something has triggered a desire to pursue revolutionary action over discontented acceptance of the status quo. Ideologically, the simulated revolution lacked content beyond a vague desire for enhanced worker solidarity and liberation from capitalist exploitation. To advance this project, future research should aim to gather empirical data that can reduce reliance on proxy parameters and improve modeling accuracy, and explore through interviews and participatory research methods, what concrete revolutionary goals workers and managers might envision. Bridging the gap between discontent and solidarity requires more than dissatisfaction; it requires direction, clarity, and shared vision. A management revolution is possible, but it will never happen without these things.
5. Conclusion
Simulations are not reality. Even as simplified representations, simulations can still produce results that are suggestive as to what is likely and what is possible. The results of this simulation of management ideology suggest that it is highly unlikely that most managers will ever become revolutionary while operating under the dominant capitalist ideology. At no point in the simulation did the average proportion of revolutionary managers exceed that of reactionary managers. The steady state was achieved at a point where 68% of the management agents operated as reactionaries and 32% as revolutionaries. However, revolutions do not require a majority to be successful. A galvanized minority can transform reality. If the critical mass required for a successful revolution is somewhere between 3.5% and 30%, then this is achievable, even under socio-politico-organizational conditions that are antagonistic to that aim. A management revolution is possible. Time will tell if it is forthcoming.
Appendix A: RStudio Simulation Code
# Libraries
library(dplyr)
library(ggplot2)
library(triangle)
# Update ideology for a single agent
update_ideology <- function(i, agents, radicalized_worker_pressure, solidarity, peer_influence, reactionary_pressure, capitalist_pressure) {
agent <- agents[i, ]
revolutionary_neighbors <- sum(agents$ideology == "revolutionary")
peer_ratio <- revolutionary_neighbors / nrow(agents)
if (agent$ideology == "reactionary") {
pressure <- radicalized_worker_pressure * peer_ratio +
solidarity * peer_ratio +
peer_influence * peer_ratio
if (runif(1) < pressure + agent$radicalization) {
return("revolutionary")
} else {
return("reactionary")}
} else {
reversion_chance <- (reactionary_pressure + capitalist_pressure) * (1 - agent$radicalization)
if (runif(1) < reversion_chance) {
return("reactionary")
} else {
return("revolutionary")}}}
# Remove and replace revolutionaries
remove_and_replace_revolutionaries <- function(agents, removal_prob) {
is_revolutionary <- agents$ideology == "revolutionary"
to_remove <- is_revolutionary & runif(nrow(agents)) < removal_prob
num_removed <- sum(to_remove)
agents <- agents[!to_remove, ]
if (num_removed > 0) {
new_ids <- max(agents$id) + seq_len(num_removed)
new_agents <- data.frame(
id = new_ids,
ideology = rep("reactionary", num_removed),
radicalization = runif(num_removed, 0, 1))
agents <- rbind(agents, new_agents)}
return(agents)}
# Run one simulation
run_simulation <- function(n_agents, n_steps) {
initial_revolutionaries <- rtriangle(1, a = 0.00, b = 0.06, c = 0.00)
peer_influence <- rtriangle(1, a = 0.75, b = 0.89, c = 0.75)
capitalist_pressure <- rtriangle(1, a = 0.4, b = 0.7, c = 0.7)
radicalized_worker_pressure <- rtriangle(1, a = 0.00, b = 0.14, c = 0.00)
solidarity <- rtriangle(1, a = 0.06, b = 0.10, c = 0.06)
reactionary_pressure <- rtriangle(1, a = 0.18, b = 0.30, c = 0.30)
removal_probability <- rtriangle(1, a = 0.30, b = 0.55, c = 0.55)
agents <- data.frame(
id = 1:n_agents,
ideology = sample(c("reactionary", "revolutionary"), n_agents,
replace = TRUE,
prob = c(1 - initial_revolutionaries, initial_revolutionaries)),
radicalization = runif(n_agents, 0, 1))
history <- data.frame(
step = 0,
reactionaries = sum(agents$ideology == "reactionary") / n_agents,
revolutionaries = sum(agents$ideology == "revolutionary") / n_agents)
for (step in 1:n_steps) {
new_ideologies <- sapply(1:nrow(agents), function(i) {
update_ideology(i, agents, radicalized_worker_pressure, solidarity,
peer_influence, reactionary_pressure, capitalist_pressure)})
agents$ideology <- new_ideologies
agents <- remove_and_replace_revolutionaries(agents, removal_probability)
history <- rbind(history, data.frame(
step = step,
reactionaries = sum(agents$ideology == "reactionary") / n_agents,
revolutionaries = sum(agents$ideology == "revolutionary") / n_agents))}
return(history)}
# Simulation Parameters
target_ci_width <- 0.005
min_runs <- 30
max_runs <- 1000
batch_size <- 10
n_agents <- 100
n_steps <- 50
# Convergence Control
sim_results <- data.frame()
ci_tracking <- data.frame(n_runs = integer(), ci_width = numeric())
n_runs <- 0
ci_width <- Inf
while ((ci_width > target_ci_width || n_runs < min_runs) && n_runs < max_runs) {
new_batch <- do.call(rbind, lapply(1:batch_size, function(run_id) {
run_simulation(n_agents, n_steps) %>%
mutate(run = n_runs + run_id)}))
sim_results <- rbind(sim_results, new_batch)
n_runs <- n_runs + batch_size
final_step_data <- sim_results %>%
filter(step == n_steps) %>%
group_by(run) %>%
summarise(revolutionaries = mean(revolutionaries), .groups = "drop")
se <- sd(final_step_data$revolutionaries) / sqrt(nrow(final_step_data))
ci_width <- 1.96 * se
ci_tracking <- rbind(ci_tracking, data.frame(n_runs = n_runs, ci_width = ci_width))
cat(sprintf("Runs: %d | Current 95%% CI Width: %.4f\n", n_runs, ci_width))}
cat(sprintf("\n Converged after %d simulation runs (to 95%% CI width ≤ %.4f)\n",
n_runs, target_ci_width))
# Estimate steady-state proportions from final step
steady_state_summary <- final_step_data %>%
summarise(
mean_revolutionaries = mean(revolutionaries),
sd_revolutionaries = sd(revolutionaries),
min_revolutionaries = min(revolutionaries),
max_revolutionaries = max(revolutionaries),
mean_reactionaries = 1 - mean(revolutionaries),
sd_reactionaries = sd(revolutionaries),
min_reactionaries = 1 - max_revolutionaries,
max_reactionaries = 1 - min_revolutionaries)
print(steady_state_summary)
# Summary Stats
summary_stats <- sim_results %>%
group_by(step) %>%
summarise(
mean_reactionaries = mean(reactionaries),
sd_reactionaries = sd(reactionaries),
mean_revolutionaries = mean(revolutionaries),
sd_revolutionaries = sd(revolutionaries),
.groups = "drop"
) %>%
mutate(
upper_react = mean_reactionaries + sd_reactionaries,
lower_react = mean_reactionaries - sd_reactionaries,
upper_revolt = mean_revolutionaries + sd_revolutionaries,
lower_revolt = mean_revolutionaries - sd_revolutionaries)
label_data <- summary_stats %>%
filter(step == max(step)) %>%
select(step, mean_reactionaries, mean_revolutionaries)
Appendix B: RStudio Data Visualization Code
# Figure 1: CI Convergence Plot
ggplot(ci_tracking, aes(x = n_runs, y = ci_width)) +
geom_line(color = "black", size = 1.2) +
geom_hline(yintercept = target_ci_width, linetype = "dashed", color = "gray40") +
annotate("text", x = max(ci_tracking$n_runs), y = target_ci_width + 0.0025,
label = paste("Target CI Width =", target_ci_width),
hjust = 1.1, color = "gray40", size = 4, family = "Times New Roman") +
labs(
title = "Convergence of 95% Confidence Interval Width",
x = "Number of Simulation Runs",
y = "CI Width (95%)") +
theme_minimal() +
theme(
text = element_text(family = "Times New Roman"),
plot.title = element_text(size = 14, family = "Times New Roman"))
# Figure 2: Management Ideology Assessment Plot
ggplot(summary_stats, aes(x = step)) +
annotate("rect", xmin = 50, xmax = Inf, ymin = -Inf, ymax = Inf, fill = "white") +
geom_ribbon(aes(ymin = lower_react, ymax = upper_react), fill = "gray80", alpha = 0.5) +
geom_ribbon(aes(ymin = lower_revolt, ymax = upper_revolt), fill = "pink", alpha = 0.5) +
geom_line(aes(y = mean_reactionaries), color = "black", linewidth = 1.2) +
geom_line(aes(y = mean_revolutionaries), color = "red", size = 1.2) +
geom_text(data = label_data,
aes(x = step + 0.5, y = mean_reactionaries),
label = "Reactionary", color = "black", hjust = 0, size = 5, family = "Times New Roman") +
geom_text(data = label_data,
aes(x = step + 0.5, y = mean_revolutionaries),
label = "Revolutionary", color = "red", hjust = 0, size = 5, family = "Times New Roman") +
labs(title = paste("Management Ideology (Mean ± SD) —", n_runs, "Simulation Runs"),
y = "Percentage of Management Agents by Ideology", x = "Time Step") +
theme_minimal() +
theme(
text = element_text(family = "Times New Roman"),
plot.title = element_text(family = "Times New Roman"),
axis.title = element_text(family = "Times New Roman"),
axis.text = element_text(family = "Times New Roman")) +
scale_x_continuous(
breaks = seq(0, n_steps, by = 10),
limits = c(0, n_steps + 5))
# Figure 3: Revolutionary Critical Mass Assessment Plot
summary_stats_trunc <- summary_stats %>% filter(step <= 50)
ggplot(summary_stats_trunc, aes(x = step)) +
annotate("rect", xmin = 0, xmax = 50, ymin = 0.035, ymax = 0.30, fill = "gray50", alpha = 0.5) +
geom_ribbon(aes(ymin = lower_revolt, ymax = upper_revolt), fill = "pink", alpha = 0.5) +
geom_line(aes(y = mean_revolutionaries), color = "red", size = 1.2) +
annotate("text", x = 31, y = 0.16,
label = "Minimum Critical Mass Zone for Successful Management Revolution",
color = "black", size = 5, family = "Times New Roman", hjust = 0.7, vjust = 1.0) +
labs(title = paste("Revolutionary Critical Mass Assessment (Mean ± SD) —", n_runs, "Simulation Runs"),
y = "Percentage of Revolutionary Management Agents",
x = "Time Step") +
theme_minimal() +
theme(
text = element_text(family = "Times New Roman"),
plot.title = element_text(family = "Times New Roman"),
axis.title = element_text(family = "Times New Roman"),
axis.text = element_text(family = "Times New Roman")) +
scale_y_continuous(labels = scales::percent_format(accuracy = 0.1), expand = c(0, 0)) +
scale_x_continuous(breaks = seq(0, 50, by = 10), limits = c(0, 50)) +
coord_cartesian(ylim = c(0, max(summary_stats_trunc$upper_revolt, na.rm = TRUE) * 1.1))