Theology after the Birth of God: Atheist Conceptions in Cognition and Culture
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In other words, during periods when anxiety is being decreased the religiosity of both the majority and minority group increases, but this overall increase in religiosity is driven by AP more than SP. These dynamics highlight the importance of the role of rituals in decreasing anxiety and increasing AP w.
The performance of rituals is a unique feature of the model that enables agents to mediate the effects of hazards through social interaction with other hyper-vigilant members within their group. The increase in religiosity is driven by AP as opposed to SP because AP is increased with respect to the ritual cluster while SP is increased with respect to the group. Recall, SP is increased with respect to the group because it reflects the act of forming a cluster with in-group members.
AP is increased with respect to the ritual cluster because it reflects the construction of a supernatural explanation of those hazards which is specific to the agents in the cluster.
Philosophy of Religion
The increases with respect to the ritual cluster AP are larger than the increases with respect to the group SP because it is likely that at least one agent in a ritual cluster has participated in a ritual cluster before. This occurs because the ritual cluster disbands when the anxiety level of each agent in the cluster is below the hyper-vigilance threshold. This leaves at least one agent with increased AP and SP but with an anxiety level just below the hyper-vigilance threshold.
As a result, this agent is likely to quickly experience a hazard and need to re-enter a ritual cluster. Thus, when AP is increased in a ritual cluster the magnitude of the increase is often influenced by the already high AP value of an agent who has previously participated in ritual clusters. However, when SP is increased it is always calculated with respect to the average value of the whole group. This reduces the influence of the magnitude of an increase in SP from agents who have previously participated in ritual clusters.
The result is the emergence of influence from agents who regularly exceed the hyper-vigilance threshold on the AP dimension of the religiosity of the group. In the next subsection we identify the specific conditions that need to be present for mutually escalating xenophobic anxiety to exist and be maintained over a period of time. To identify the conditions associated with mutually escalating xenophobic anxiety, we use a trace validation technique specifically designed for analyzing agent-based models Gore et al. We provide an overview of the trace validation technique here, but it is described in more detail in Appendix A.
This technique utilizes a structured approach to capture data throughout execution i. These conditions are then used to quantify the extent to which combinations of agent and model characteristics cause unexpected model behaviors based on an output of interest, such as mutually escalating xenophobic anxiety Gore et al.
Our use of the term cause refers to model inputs that generate an output of interest i. This is similar to the use of the term in statistics Cox as opposed to the use of the term cause in philosophy of science as described in Bunge The extent to which each generated condition contributes to mutually escalating xenophobic anxiety intervals is quantified by two measures: correlation and coverage. These measures are aggregated into a single score called suspiciousness. The name suspiciousness originated in the field of statistical debugging in software engineering because it was used to automatically localize faults in computer programs.
The formulas for each measure are provided in Appendix A. The coverage measure is the percentage of all mutually escalating Intervals that exhibit the specified condition. The suspiciousness measure combines and balances the specificity and the coverage measures using the harmonic mean. Each measure has a maximum value of 1. A suspiciousness value of 1. In other words, the condition completely distinguishes all mutually escalating times series from any other escalating time series. The existence of such a condition is not guaranteed.
However, conditions with higher suspiciousness scores will provide more separation between mutually escalating intervals and other escalating intervals than conditions with lower suspiciousness scores. Our trace validation approach scores each condition generated using the escalating intervals captured during the parameter sweep.
This procedure iteratively applies to all intervals starting with a minimum length of 2 time steps and ending with the maximum length 9. A detailed explanation of how these conditions i. Given the measure of suspiciousness, we assume the conditions that create mutually escalating xenophobic anxiety will have two properties: 1 their suspiciousness score will continually improve as the length of the interval increases, and 2 the suspiciousness score for the longest intervals should be close to 1. The rationale for the first property is that longer intervals reflect stronger expressions of mutually escalating xenophobic anxiety, and so a condition responsible for creating mutually escalating xenophobic anxiety should increase its suspiciousness measure as the length of the time series increases.
The rationale for the second property is that the condition for strong expressions of mutually escalating xenophobic anxiety should be present in all of those intervals and only those intervals. Our selection of these properties is an attempt to filter out noise in the simulation created by the use of stochastics and to highlight the signal strength of the different rules in the simulation that govern agent behavior.
After generating and iteratively scoring each of the conditions, one condition increases suspiciousness as minimum length of the intervals increases, with a peak suspiciousness score of 1. Figure 9 demonstrates the increase of this condition's suspiciousness shown in green up to a peak of 1. The steady rise of the suspiciousness score demonstrates that the green condition is more frequently met in mutually escalating times series as the length of the time series increases. Figure 9 also suggests that social and contagion threats green and blue lines are more impactful than natural and predatory threats red line on the escalation of anxiety.
This result is readily interpretable. Mutually escalating xenophobic anxiety within the model is generated under the following three conditions: 1 When the majority and minority group are created there is not too large a disparity in size between them; 2 When the average intensity of a social hazard an agent experiences meets or exceeds the threshold the agent has for social hazards; and 3 When the average intensity of a contagion hazard an agent experiences meets or exceeds the threshold the agent has for contagion hazards.
The combination of these circumstances creates an environment where agents in the majority and the minority groups regularly identify agents from the other group within a specified radius and perceive them as social and contagion threats. Mutually escalating intervals are produced because both groups are operating under circumstances where they are likely to experience hazards, which increases anxiety. Figure 9 also demonstrates that other, even somewhat similar, conditions do not provide as effective an explanation for mutually escalating anxiety.
The analysis in Figure 9 further shows that the intensity and threshold of social and contagion hazards are more responsible for creating mutually escalating xenophobic anxiety than natural and predation hazards. This makes sense because natural and predation hazards occur randomly at each time step and are unaffected by the percentage of agents in the majority or minority group. Social and contagion hazards, in contrast, are triggered as agents search for out-group agents during each time step. While random natural and predation hazards that meet or exceed the threshold of agents will increase the number of perceived threats, it is unlikely that predation and natural hazards alone will be enough to cause agents in both groups to cross the threshold in consecutive time steps, and that is what is required for mutually escalating xenophobic anxiety to arise within the model.
The trace validation analysis yields a simple explanation for how mutually escalating xenophobic anxiety emerges within the model. The agents need to be distributed into two groups not too different in size, and the simulation must produce the conditions under which agents from both groups encounter social and contagion hazards at levels of intensity that meet or exceed their thresholds for the respective hazards. Agents will then encounter others from a different group regularly and perceive them as threats, creating mutually escalating xenophobic anxiety.
It may appear as if this explanation relies only on system level properties thresholds and population proportions as opposed to interaction elements. However, upon closer inspection this is not the case. Mutually escalating xenophobic anxiety depends on there being enough agents in the minority group such that they are regularly encountered by and interact with the majority group agents.
Without these interactions, the perceived social and contagion hazards that drive mutually escalating anxiety will not occur. Given knowledge of previous research, and access to Figure 9 , this explanation may seem so simple as to appear trivial. However, many emergent behaviors generated from agent-based models seem obvious once one knows how they arise. Our use of trace validation highlighted the conditions that cause mutually escalating xenophobic anxiety to emerge in the model, and these are consistent with the conditions discovered in other empirical research on the role of social conflict in increasing anxiety and engendering violence Reed et al.
These findings also provide some confidence that our model can produce valuable insights into real-world intergroup conflicts such as those that occurred during the Gujurat riots. We are not claiming to have produced a novel explanation, but a novel computational model that illuminates some of the micro-level mechanisms at work in generating the macro-level phenomenon of mutually escalating xenophobic anxiety. Concerns about external, construct, and internal validity all affect the plausibility of our model.
In addition to these validity concerns, our model has a number of limitations. Here we review each of these areas and discuss how they relate to our model. Concerns about external validity arise when the results of the model cannot be generalized. The results of our current model cannot be generalized to explain specific occurrences of or to forecast mutually escalating xenophobic anxiety. However, this does not mean that the model bears no relationship to the real world. It is an attempt to implement relevant theories based on empirical research that has helped to explain phenomena of the sort described in our two examples in the introduction.
Terror management theory TMT , social identity theory SIT and identity fusion theory IFT have been used to describe how conflict between religious groups increases anxiety, and can even lead to a willingness to commit acts of violence on behalf of an in-group. Concerns about construct validity are related to the appropriateness of the measures used to represent the entities in our model.
While our model reflects a novel generative explanation of mutually escalating xenophobic anxiety, it is limited by our assumptions and choice of abstractions. First, the system and agents within our model are mostly static. By this we mean that they continue to behave according to the same mechanisms for a whole simulation run. In future work we will explore the interplay that results from adding more individual behavior mechanisms and network dynamics, taking advantage of existing research in computational sociology.
Second, the initial anxiety level of each agent is 0 within our model. One could note that this might create a potential for path dependence and atypical system convergence behavior. We have explored whether initializing agent anxiety to a uniform number in [0,1] changes our results in Section 5 ; and it does not. However, in future work we will explore whether the results are sensitive to more varied initializations of agent anxiety.
Third, the social networks in our model follow the Watts-Strogatz model to describe in-group relationships. While this seems reasonable given that in-group agents interact by conducting rituals with one another, it is not the only type of social network that may be applicable. We have investigated whether the results of the simulation experiments are sensitive to this assumption by exchanging the Watts-Strogatz network to describe in-group relationships with Random networks and Preferential Attachment networks. These changes do not have an effect on our results.
In future work we will explore whether the results are sensitive to other social network models of in-group relationships. Fourth, the intensity of the hazards within our model are generated using a triangular distribution. One could argue that this type of distribution is not observed in reality. We have investigated using a truncated normal distribution instead of a triangular distribution. The truncated normal distribution also meets our requirements of a non-uniform distribution with a minimum, maximum and central tendency. This change does not have a material effect on our reported results.
Finally, the largest concern related to construct validity involves equifinality. The construction of our model required us to make a series of reasonable assumptions to fill specificity gaps. While we did our best to address each gap with the most reasonable assumptions, we did not exhaustively explore all of the additional factors that could have been included. For example, the model omits the role of political actors who mobilize religious groups to intensify and calcify perceived differences between them.
It is possible that our interest in this topic may have biased our model construction.
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The technical choices in our model serve as recommendations for other researchers tasked with similar problems, and we welcome debate over alternatives for model construction. In future work we will use more input from qualitative research to inform new versions of the model. Internal validity concerns arise when factors affect the dependent variables without the modelers' knowledge. It is possible that some implementation flaws could have affected the evaluation results.
However, the algorithms we used within our model passed several internal code reviews and the analysis presented in the Results Section combines the output of 20, model executions. In addition, we used established libraries whenever possible to minimize the amount of new code that would be included in the model or the analytical tools we developed. The experimental results of the model described in this article expand our understanding of the conditions under which - and the mechanisms by which - mutual xenophobic anxiety between religious groups can emerge and escalate.
Its causal architecture, based on several leading theories about the relationships among components of religiosity and psychological and social anxiogenic factors generated macro-level phenomena from the micro-level behaviors and interactions of artificial agents in a simulated environment. The trace validation techniques we used show that the most common conditions under which longer periods of mutually escalating xenophobic anxiety occur are those in which the difference in the size of the groups is not too large and the agents experience social and contagion hazards at a level of intensity that meets or exceeds their thresholds for those hazards.
Under these conditions agents will encounter out-group members more regularly, and perceive them as threats, generating mutually escalating xenophobic anxiety. While the model has limitations related to its external, internal, and construct validity, it serves as a platform for future work in this domain. Our approach to trace validation employs predicates that are used in statistical debuggers. Statistical debuggers isolate the causes of software bugs using a set of inputs, corresponding execution traces, and a labeling of the execution traces as passing or failing Liblit et al.
The execution traces typically reflect the coverage of individual statements. The debuggers assign suspiciousness scores to statements to guide developers in locating faults. The suspiciousness of a statement balances these two rate measures via the harmonic mean. Developers examine the statements in decreasing order of suspiciousness until the fault is discovered. For the approach to be effective, faulty statements must generally have higher suspiciousness scores than non-faulty statements.
In addition to profiling program statements, most statistical debuggers employ conditional propositions, or predicates, to record the values assigned to variables in an execution trace. For example, three predicates can be instrumented for every assignment statement in a program to test if a value being assigned to a variable is greater than, less than, or equal to zero. The suspiciousness of these predicates is calculated using the failing execution traces where the predicate is true, the total number of execution traces where the predicate is true and the total number of failing execution traces.
The addition of predicates including those that are more complex than the three described above enables statistical debuggers to analyze relationships within and among variable values. In theory and in practice this has been shown to improve effectiveness of the statistical debugging Liblit ; Gore et al. Next, we describe the different types of predicates and how these predicates can be combined. Statistical debuggers employ two different types of predicates single variable , scalar pairs at two different levels of specificity static and elastic to localize bugs. The choice of type and the specificity-level defines a unique combination of conditions related to the variable s that the predicate captures.
Two or more predicates can also be combined by generating compound predicates to gather insight about a variable's behavior at an additional level of granularity. Here we review predicate types, their specificity levels, and describe how they can be combined in a compound predicate. Single variable predicates can be created at two levels of specificity: the static level and the elastic level.
The most basic single variable predicates are static. Scalar pair predicates capture the important relationships between two variables that elude single variable predicates. The most basic scalar pair variables are static. Once created a compound predicate can be combined with another compound predicate. Work in the field of software engineering has shown that there is not a significant benefit to combining compound predicates together more than three times Arumuga Nainar et al. To perform a trace validation of this ABM, we start by specifying one or more outputs of interest within the ABM for the validation.
Next, we specify the range of values under which each output is considered valid.
In the context of this paper this is mutually escalating anxiety occurring over a time series of at least length two. Then, we identify the entities of interest within the model to trace. These are specified in the Results Section. Next, we run the simulation for a set of inputs based on a parameter sweep. The parameter sweep is the sweep described in Section 4. Finally, we collect the suspiciousness scores for each predicate i. The predicates and suspiciousness scores capture the relationship between: 1 the conditions within and among the entities and the presence of the output i.
This section provides download links to supplementary information associated with our work and model. The AnyLogic. Introduction Does religion play a causal role in promoting anxiety between groups? Figure 1. Highlighted periods 3 years or more of mutual escalation of anxiety, manifested in shootings, in Northern Ireland from Social conflict models Other models have explored the role of cultural differentiation and boundary clarification in shaping the dynamics of ethnic anxiety and violence Lim et al.
Background Our modeling of intergroup conflict focuses on the role of "religion", a term whose definition and use is highly contentious within and across many disciplines. Figure 2. Visual depiction of AP and SP. Terror Management Theory TMT The model described below builds on an earlier model we developed to explore the relationship between mortality salience and religiosity, as defined above Shults et al.
Identity Fusion Theory IFT This model's architecture is also informed by identity fusion theory IFT , another important research program bearing on the escalation of intergroup anxiety. Figure 3. Variable dependencies within the model that allow for identifying conditions under which mutually escalating xenophobic anxiety between religious groups emerge. Figure 4. Three series of mutually escalating anxiety within a single model run.
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Figure 5. Note that connections only exist between agents in the same group. Figure 6. Decision tree for agents each time step. Figure 7. Results Frequency of escalating anxiety First we investigate the extent to which the model produces mutually escalating xenophobic anxiety.
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Figure 8. Frequency of the three different types of mutually escalating anxiety traces. Increase in AP 0. Isolating the causes of mutually escalating xenophobic anxiety To identify the conditions associated with mutually escalating xenophobic anxiety, we use a trace validation technique specifically designed for analyzing agent-based models Gore et al. Figure 9. Analysis of conditions causing mutually escalating anxiety. Model Validity and Limitations Concerns about external, construct, and internal validity all affect the plausibility of our model.
External validity Concerns about external validity arise when the results of the model cannot be generalized. Construct validity Concerns about construct validity are related to the appropriateness of the measures used to represent the entities in our model. Internal validity Internal validity concerns arise when factors affect the dependent variables without the modelers' knowledge. Conclusion The experimental results of the model described in this article expand our understanding of the conditions under which - and the mechanisms by which - mutual xenophobic anxiety between religious groups can emerge and escalate.
Mathematical models in social psychology. Advances in Experimental Social Psychology , 3, 1— Social learning and deviant behavior: A specific test of a general theory. MRP has also been working to produce a computer system with a user-friendly interface for developing simulations without needing to know any programming languages. This will empower scholars who have not been trained in computer science to actively participate in computer modeling.
MRP has been a powerful learning process for all involved, with humanities scholars learning how to theorize in a computational language and computer scientists gaining deeper insight into the complexity of human dynamics. For more information about the MRP, the best place to start is the documentary series about the project created by our terrific documentarian Jenn Lindsay click here.
Wood, C. Denver: University of Colorado Press, Modeling the cognitive and coalitional mechanisms that promote atheism. Cambridge Scholars Press, in press. On his blog Leron gracefully referred me to his books when I asked about the difference between his Iconoclastic Deleuzian atheism and say a Whiteheadian process-relational panentheism. Fair enough. I mean I might like to make a distinction between anthropomorphism and personification but hey, like I said, he makes a challenging argument and he might be right, maybe Scandinavian folks are the next step in human psycho-social evolution….
Nice post. Why do so many people who should know better insist on confusing religion with anthropomorphic theism? Do they not read anything? Is research no longer important for an author? As for the idea that science is better for the environment then religion, this depends on keeping ones eyes well and truly shut and believing ten impossible things before every breakfast. You are commenting using your WordPress.
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