Association for Behavior Analysis International

More than 34 million premature deaths in 2017 occurred globally due to accidents and conditions detrimental to health. Risk management consists of proven methods for reducing fatalities due to accidents in the industry. Past incidents and future potential accidents are two main information sources used in risk management. The former is provided by accident investigation, and the latter by risk analysis where the risk potential of hazards are estimated. Industrial risks are reduced as various forms of risk management have been applied. Industrial risk is however only a minor part of the total risk. Societal risks may increase due to global warming, overpopulation and migration, new diseases and reduced effects of antibiotics. The development of robots and artificial intelligence may introduce new and unknown hazards for both industry and society. The need for more effective risk management is higher than ever. Radical behavior analysis and biased decision-making as studied in cognitive behavior science have so far not been systematically applied in risk management systems. Can lessons learned from behavior analysis and the cognitive bias research improve risk management, for both industry and society?

Although true behavioral effects of hormonal fluctuations of the menstrual cycle have been established, many attributed effects, such as those on cognitive bias, a well-known phenomenon in the realm of judgment and decision making, have not been verified. In a novel approach that eschews hypothetical scenarios, we directly observe regularly-cycling women, and a male control group, as they play multiple sessions of a computer game embedded with concurrent-operant choice schedules that result in actual, real-time monetary gains and losses. When losses occur against a baseline of gains, they alter choice dramatically. The relative effects of gains and losses are analyzed to establish a gain-loss differential in each of three distinct phases of the menstrual cycle. We determine if those phases affect how likely women are to avoid losses rather than to pursue gains of money. We hypothesize that the distinct phases of the menstrual cycle will have a differential effect on choice behavior involving money. It is necessary to distinguish attributed behaviors from those that actually fluctuate systematically with menstrual cycle phases, thus, allowing women to better navigate a behavioral domain that includes social roles and expectations but also true menstrual cycle effects.