Humans have always been exposed to different kinds of threats. These threats have the ability to influence group behaviour and can have an effect on individual or collective welfare. While evidence...Show moreHumans have always been exposed to different kinds of threats. These threats have the ability to influence group behaviour and can have an effect on individual or collective welfare. While evidence suggests that threat exposure can increase cooperation and collective action, there is also evidence that suggests that threats actually break down cooperation. Furthermore, peer punishment is generally seen as a method to maintain cooperation, though usually at the cost of wasted resources. The effects of a shared threat on social decision-making in groups and the impact of peer punishment will be examined in order to test whether the presence of a common threat renders the use of peer punishment obsolete. Individuals (N = 180) in groups of three were exposed to the threat of electric shocks. Heart rate and skin conductance were continuously measured while participants decided how much to contribute to a public good. Half of the participating groups also got the opportunity to deduct resources from another participant during feedback phases.Show less
Social dilemmas arise when individual and collective interests conflict. Some crises-like social dilemmas, such as the COVID-19 pandemic, comprise two parts: people must cooperate to prevent a...Show moreSocial dilemmas arise when individual and collective interests conflict. Some crises-like social dilemmas, such as the COVID-19 pandemic, comprise two parts: people must cooperate to prevent a disaster (public bad) but once prevented, i.e., a turning point was reached, positive externalities are generated (public good). Our study aims to expand the literature by studying cooperation, coordination, and motivation in such crises-like situations. We formulated the Adversity-Opportunity model (AOM) as a modified public goods game, introducing a negative initial group account to model "turning a bad into a good” and the turning point. In a mixed-factorial experiment, we measured participants' social value orientation and afterwards allocated them into three conditions. While contributions in the AOM condition could leave/turn the group account negative/positive, it could only remain negative (Public Bad condition) or is only shared when positive (Public Good condition). Within subjects, we varied the negative initial group account, while prompting expected and most appropriate contribution after each decision. Our results showed consistently high cooperation in the AOM across turning points, while social dispositions were a significant predictor of cooperation. We argue that when cooperation always is continuously beneficial, coordination is not required, and people cannot deduct a convergent collective and individual interest. A common interest to avoid over- or underspending could have enabled participants to form expectations and thus partially coordinate their choices. A public bad fostered marginally larger contributions than a public good interaction, after accounting for dispositions. Further research is necessary to confirm our findings and assess follow-up questions.Show less
Artificial Intelligence is a sophisticated emerging technology that cybercriminals have increasingly been using maliciously to facilitate their attacks. As the private and public sector are key...Show moreArtificial Intelligence is a sophisticated emerging technology that cybercriminals have increasingly been using maliciously to facilitate their attacks. As the private and public sector are key targets of these attacks, collaboration is called for. This study dissects the divergent perceptions of cybercrime in both sectors which have inhibited cooperation in the past during the growth of the Internet, then aligns these scattered views to build common ground to establish a cooperation for this up-and-coming threat. The societal aim is to avoid repeating previous mistakes encountered at the birth of cybercrime. This thesis asks the following question: To what extent does the perception of risk of malicious use of AI by cybercriminals differ within the public and private sector? After conducting a qualitative analysis of nine semi-structured interviews, the thesis finds that distinct perceptions about cybercrime between sectors is what is at the heart of the stagnation. But neither public or private organisations can solve the issue of the upcoming AI cybercriminal threat and reach desired goals without each other as each sector lacks some elements complemented by the other. The differences found in this research can shape the basis of a cooperation against AI-Cybercrimes between the public and private sector.Show less
