This study examines the influence of AI-driven chatbots on decision-making within a one-shot Prisoner’s Dilemma context. By manipulating chatbot biases toward either cooperation or defection, and varying user awareness of these biases, the study observes significant e!ects on participants’ choices and beliefs. Notably, contrary to expectations, awareness of biases amplifies rather than diminishes the influence on cooperative behavior. These findings underscore
the roles AI systems play in shaping strategic human decisions and highlight the need for transparency in their deployment.
Gender differences in competitiveness are typically examined in relation to individual labor market outcomes. However, Becker’s (1973) marriage theory suggests that traits like competitiveness can also influence a partner’s income through mating effects (partner selection based on income) and cross-productivity effects (one partner’s traits enhancing the other’s income). Using Dutch household panel data in a context of high female part-time employment, we find that both men’s and women’s competitiveness predict their own future income. However, only women’s competitiveness has a cross-productivity effect. To isolate this effect, we employ a rich set of personality controls and a novel couple fixed effects approach addressing the limitation of single-measurement competitiveness. We find no evidence of mating effects for women’s competitiveness. Men’s competitiveness shows neither mating nor cross-productivity effects. The cross-productivity effect of women’s competitiveness is not driven by household specialization: only women’s work hours increase with their competitiveness, not men’s. Women’s competitiveness does not reduce their partner’s time spent on housework or childcare. In contrast, men’s competitiveness increases women’s housework and reduces their childcare time. Financial satisfaction moderates the positive effect of women’s competitiveness on their partner’s income. Overall, this cross-productivity effect accounts for 11–23% of the gender income gap among partnered individuals
