Institutions for Intuitive Man

By its critics, the rational choice model is routinely accused of being unrealistic. One key objection has it that, for all nontrivial problems, calculating the best response is cognitively way too taxing, given the severe cognitive limitations of the human mind. If one confines the analysis to consciously controlled decision-making, this criticism is certainly warranted. But it ignores a second mental apparatus. Unlike conscious deliberation, this apparatus does not work serially but in parallel. It handles huge amounts of information in almost no time. It only is not consciously accessible. Only the end result is propelled back to consciousness as an intuition. It is too early to decide whether the rational choice model is ultimately even descriptively correct. But at any rate institutional analysts and institutional designers are well advised to take this powerful mechanisms seriously. In appropriate contexts, institutions should see to it that decision-makers trust their intuitions. This frequently creates a dilemma. For better performance is often not the only goal pursued by institutional intervention. Accountability, predictability and regulability are also desired. Sometimes, clever interventions are able to get them both. Arguably, the obligation to write an explicit set of reasons for a court decision is a case in point. The judge is not obliged to report the mental processes by which she has taken her decision. Justification is only ex post control. Intuitive decision-making is even more desirable if the underlying social problem is excessively complex (NP hard, to be specific), or ill-defined. Sometimes, it is enough for society to give room for intuitive decision-making. For instance, in simple social dilemmas, a combination of cheater detection and punishing sentiments does the trick. However, intuition can be misled. For instance, punishing sentiments are triggered by a hurt sense of fairness. Now in more complex social dilemmas, there are competing fairness norms, and people intuitively choose with a self-serving bias. In such contexts, institutions must step in so that clashing intuitions do not lead to social unrest.intuition, consciousness, rational choice, heuristics, ill-defined social problems, institutions

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.Comment: 10 page

Tailoring persuasive health games to gamer type

Persuasive games are an effective approach for motivating health behavior, and recent years have seen an increase in games designed for changing human behaviors or attitudes. However, these games are limited in two major ways: first, they are not based on theories of what motivates healthy behavior change. This makes it difficult to evaluate why a persuasive approach works. Second, most persuasive games treat players as a monolithic group. As an attempt to resolve these weaknesses, we conducted a large-scale survey of 642 gamers' eating habits and their associated determinants of healthy behavior to understand how health behavior relates to gamer type. We developed seven different models of healthy eating behavior for the gamer types identified by BrainHex. We then explored the differences between the models and created two approaches for effective persuasive game design based on our results. The first is a one-size-fits-all approach that will motivate the majority of the population, while not demotivating any players. The second is a personalized approach that will best motivate a particular type of gamer. Finally, to make our approaches actionable in persuasive game design, we map common game mechanics to the determinants of healthy behavior

Man and Machine: Questions of Risk, Trust and Accountability in Today's AI Technology

Artificial Intelligence began as a field probing some of the most fundamental questions of science - the nature of intelligence and the design of intelligent artifacts. But it has grown into a discipline that is deeply entwined with commerce and society. Today's AI technology, such as expert systems and intelligent assistants, pose some difficult questions of risk, trust and accountability. In this paper, we present these concerns, examining them in the context of historical developments that have shaped the nature and direction of AI research. We also suggest the exploration and further development of two paradigms, human intelligence-machine cooperation, and a sociological view of intelligence, which might help address some of these concerns.Comment: Preprin

Shall I post this now? Optimized, delay-based privacy protection in social networks

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-016-1010-4Despite the several advantages commonly attributed to social networks such as easiness and immediacy to communicate with acquaintances and friends, significant privacy threats provoked by unexperienced or even irresponsible users recklessly publishing sensitive material are also noticeable. Yet, a different, but equally significant privacy risk might arise from social networks profiling the online activity of their users based on the timestamp of the interactions between the former and the latter. In order to thwart this last type of commonly neglected attacks, this paper proposes an optimized deferral mechanism for messages in online social networks. Such solution suggests intelligently delaying certain messages posted by end users in social networks in a way that the observed online activity profile generated by the attacker does not reveal any time-based sensitive information, while preserving the usability of the system. Experimental results as well as a proposed architecture implementing this approach demonstrate the suitability and feasibility of our mechanism.Peer ReviewedPostprint (author's final draft

Community-Based Security for the Internet of Things

With more and more devices becoming connectable to the internet, the number of services but also a lot of threats increases dramatically. Security is often a secondary matter behind functionality and comfort, but the problem has already been recognized. Still, with many IoT devices being deployed already, security will come step-by-step and through updates, patches and new versions of apps and IoT software. While these updates can be safely retrieved from app stores, the problems kick in via jailbroken devices and with the variety of untrusted sources arising on the internet. Since hacking is typically a community effort? these days, security could be a community goal too. The challenges are manifold, and one reason for weak or absent security on IoT devices is their weak computational power. In this chapter, we discuss a community based security mechanism in which devices mutually aid each other in secure software management. We discuss game-theoretic methods of community formation and light-weight cryptographic means to accomplish authentic software deployment inside the IoT device community