Modeling Women's Need For Action in Violent Relationships

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.