We are beginning to understand that intimate partner violence (IPV) and women’s decision-making about that violence are nonlinear phenomena. IPV and decision-making are influenced by variables feedforwarding upon themselves with multiple interconnected predictors and circularly causal relationships. Computer models can help us gain a systems perspective on these relationships and enable hypothesis-testing without engendering risk to women in these relationships. The purpose of this study was to develop a mathematical model of women’s decision-making concerning her violent relationship and assess the impact of random stress and her controllable behaviors on violence and decision-making. An agent-based model was created using data from couples with history of violence, based upon results of multiple time series of partner violence. To explore factors that may alter model results, eight continuous variable parameters were created based upon significant ( p ≤ .05) but discrepant (opposite directions) results from two prior time series studies. Overall, 13 unique patterns of violence in five categories were identified, but none of these categories included his violence alone without some additional influence (i.e., marital distance leading to marital distance the following day). To assess the potential impact that random stress and behaviors under her control (arguments, forgiveness, alcohol use, violence) could have on need-for-action and actions taken, the effects of variable parameter settings on these outcomes were also assessed. While random stress had little effect on outcomes, her interventions could have an impact but were pattern-specific. Her daily participation in arguments correlated with more violence. The need-for and actually taking action were at times independent of each other. This mathematical model yielded results that generally involved her violence with or without his violence. Thus, modeling partner violence and women’s decision-making is possible, yielding diverse patterns. However, the complexity of interdependent predictors unique to each relationship means that targeted interventions will need to be couple-specific.

While agent-based models (ABMs) have successfully modeled violence and women’s decision-making, they relied upon studies of her daily reports of violence and household environment; these models were not based upon descriptions of his emotions and perceptions. The purpose of this study was to improve our understanding of the triggers of violent events within violent relationships through agent-based modeling by including men’s perceptions and emotions. An agent-based model was created of couples with history of violence based upon results of a study involving multiple time series of partner violence, including couples’ daily reports of their emotions and perceptions. To explore factors that may alter model results, seven continuous variable parameters were created based upon significant ( p ≤ .05) but discrepant (opposite directions) in prior studies. To assess the potential impact that influencing factors such as random stress as well as his and her feelings and behaviors could have on violence and stalking, the impact of these factors was also assessed. Results found that, at baseline, which included no extremes in variable parameters, no violence emerged. One prior-day→same-day relationship (HerConcern→HerConcern) was particularly important in this ABM. Men’s and women’s drug use and refraining from arguments had little impact on any outcome, but his and her alcohol use, his sense of insult and her violence all had significant effects. In fact, women’s alcohol use interacted with other influencing variables and was a source of atypical patterns. In conclusion, incorporating men’s perceptions into an ABM of partner violence resulted in important differences compared with ABMs based solely on women’s input. Not only were women’s daily concerns about the effect of violence on children was critical to results, but this ABM demonstrated the complexity of partner violence in response to influences as illustrated by contextual dependence, interaction effects and synergy.

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