Cautious Defection: Group Representatives Cooperate and Risk Less than Individuals

Individuals often make decisions that affect groups, yet the propensities of group representatives are not as well understood than those of independent decision makers or deliberating groups. We ask how responsibility for group payoffs—in the absence of group deliberation—affects the choice. The experiment utilizes the Interdependent Security Dilemma paradigm. In its deterministic version, the game is identical to a finitely repeated Prisoner's Dilemma. In the stochastic version, potential losses are larger but uncertain, occurring with a predefined probability when one or both players choose to forgo investment in protection. Participants were assigned to play either as individuals, or as representatives of three‐person groups, with each member's choices equally likely to determine the group's payoffs. In the deterministic condition, individuals were more likely to invest (cooperate) than group representatives (80% vs. 62% of rounds). In the stochastic condition, the pattern was reversed as representatives were slightly, but not significantly, more likely to invest than individuals (46% vs. 40%). The significant interaction effect suggested that group representatives were not only less cooperative but also more risk averse than individuals. A follow‐up study supported this account by showing participants who construed the invest strategy as low‐risk than those who perceived this strategy to be high risk. Overall, group representatives exhibited a tendency to act more competitively and more cautiously than individuals acting alone. Copyright © 2015 John Wiley & Sons, Ltd.     

What <Emphasis Type="Italic">is</Emphasis> wrong with intelligent design?

While a great deal of abuse has been directed at intelligent design theory (ID), its starting point is a fact about biological organisms that cries out for explanation, namely “specified complexity” (SC). Advocates of ID deploy three kind of argument from specified complexity to the existence of a designer: an eliminative argument, an inductive argument, and an inference to the best explanation. Only the first of these merits the abuse directed at it; the other two arguments are worthy of respect. If they fail, it is only because we have a better explanation of SC, namely Darwin’s theory of evolution by natural selection.     

Perelman’s Pseudo-Argument as Applied to the Creationism Controversy

If you want to challenge or at least weaken the adhesion to a system of values, you can basically adopt two radically opposed rhetorical strategies. Either you will attack the system in a frontal way: for instance, fundamentalists or fascists deny any validity to democratic values and human rights. Or you will pretend to argue from within the system (by saying that you accept some of its basic premises), while subtly distorting the process of reasoning in order to get to your conclusions. If the audience is naïve or poorly informed, you will be able to defend positions that are fundamentally at odds with liberal-democratic values while seeming to argue from inside the system. I would like to show how such a process of “perverse” translation works in the context of the Darwinism/Creationism “controversy”. The attacks on the teaching of evolutionary biology began approximately one century ago. The way Creationists have argued and changed several times their rhetorical strategies seems very interesting to me, in that it exemplifies an important contemporary phenomenon, which I call “perverse translation” or “the wolf in the sheepfold”.     

自省主体对Moore-型信息的处理与融合

The main task is to discuss the issue in belief dynamics in which philosophical beliefs and rational introspective agents incorporate Moorean type new information. First, a brief survey is conducted on Moore’s Paradox, and one of its solutions is introduced with the help of Update Semantics. Then, we present a Dynamic Doxastic Logic (DDL) which revises the belief of introspective agents put forward by Lindström &; Rabinowicz. Next, we attempt to incorporate Moorean type new information within the DEL (DDL) framework, as advised by van Benthem, Segerberg et al. Though we maintain the principle of “the primacy of new information” from the literature on traditional belief revision theory, several unsuccessful ways are also presented. We then conclude that some special kind of success (weak success) can still be found in those revision processes although absolute success does not hold. At last, the relevant problem of “learnability” is re-considered through weak success.     

Who’s the boss? Arbitrating control authority between a human driver and automation system

Progress toward the fully automated highway will first require that manual and automatic control be successfully combined. Determining a combination that preserves the best performance features of human and automatic control yet allows either driver to cover for the faults of the other is a challenging problem. In this study, we invited 11 participants to drive a simulated vehicle through a course with obstacles to investigate the ability of human-automation teams to cover for human and automation faults. We developed the automation system using model predictive control and implemented three schemes under which the human would share control with the automation. In Autopilot, the human driver initiated a takeover with a button press whereas in Active Safety the automation initiated a takeover when it anticipated an obstacle collision. In Haptic Shared Control the human was free to invoke a transition by activating or relaxing muscles. In addition, we included two baseline conditions in which control was given in whole to either the human or the automation. We compared performance in the five conditions by analyzing obstacle hits and metrics related to driving maneuvers around the obstacles that were avoided. Relative to individual human or automatic driver performance, we found that control sharing reduced obstacle hits under fault conditions but also occasionally resulted in obstacle hits under no-fault conditions. Our findings further indicated that team performance suffered most under Autopilot for automation faults and suffered most under Active Safety for human faults. Haptic Shared Control supported the best overall team performance.     

Empirical and modeling study of emotional state dynamics in social videogame paradigms

The objective of this work was to study the dynamics of human emotional states in the process of social interaction in a virtual environment. The previously developed for this purpose prototypes of the virtual actor (NPC) and its virtual environment simulator “Teleport” underwent significant re-design and modification. The experimental platform was re-implemented and used in experiments with college student participants, combining electromyography, emotion recognition in facial recordings and model-based game log analysis in a social videogame paradigm. Participants interacted with two virtual actors implemented based on the eBICA cognitive architecture (Samsonovich, 2013, 2018). Positive correlations were found between eBICA model predictions and participant affects extracted from their facial expressions and facial muscle activity. Affective dynamics of social phenomena, such as the establishment of partnership or an act of betrayal, were characterized and found consistent with the model predictions. Other findings include a gradually developing emotional reaction, possibly due to the integration of appraisals of game events. Overall, obtained results confirm the eBICA model, suggesting its further extension and refinement.     

Effects of ethanol withdrawal on muricidal behavior

When naive rats of our colony were individually housed for 1 month with free access to laboratory chow and water as drinking fluid, 6% exhibited muricidal behavior. When naive rats of the same colony were similarly housed but received ethanol 20% in water as the sole source of drinking fluid (ll.5 g ethanol/kg/day), the percentage of killer rats was the same. However, when the rats were housed in similar conditions and submitted to a 1-day ethanol withdrawal, the percentage of muricidal rats increased to 25%. Ethanol intake in the same conditions as described above did not change muricidal behavior of spontaneous killers and did not induce killing behavior in nonkiller animals. GABA-mimetic agents administered IP blocked ethanol withdrawal-induced killing behavior as well as spontaneous muricidal activity.     

A real‐world rational agent: unifying old and new AI

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.     

A working memory model improves cognitive control in agents and robots

Cognition entails those mental processes enabling understanding the current situation through senses, experience, and thought, and supporting the acquisition of new knowledge. A fundamental contribution in cognition is offered by the working memory, that is a small, short-term memory containing and protecting from interference goal-relevant pieces of information. Grounding our work on biological and neuroscientific studies, we modeled and implemented working memory processes in a software model, IDRA-WM, that can simultaneously act as short-term memory and actions generator, thanks to the use of a reinforcement-driven mechanism for chunk selection. Moreover our system integrates the functions of the working memory with a basic action planner. We tested the model with robot relevant tasks to assess whether the proposed solution can learn to solve a problem on the basis of a delayed reward. The experimental results indicate that IDRA-WM is able to solve even those tasks that do not provide immediate reward after an action.     

Drivers’ self-reported responses to a potentially realistic fabricated road sign message

This article seeks to understand the potential response of drivers when they encounter compromised Dynamic Message Signs (DMS). The findings are built on the self-reported response of 4,706 participants in a Stated Preference (SP) survey conducted between November 2018 and December 2018 in the United States. The findings show the response of drivers to the “Downtown Under Terrorist Attack” message falls into route divergence, speed change, and distraction, and the likelihood of route divergence, distraction, and a slowdown is significantly more than either stopping or speeding up. The possibility of a response, however, varies depending on socioeconomic characteristics, attitudinal characteristics, and driving behavior. It is highlighted (1) female and young drivers are more probable to detour, to change speed, or to be distracted, (2) drivers who are familiar with DMS, encounter it frequently, or pay attention to its content, have a higher chance of detouring or changing speed, while they are less likely to be distracted, and (3) technology-friendly drivers are likely to detour or slow down. From the distraction model, it is further inferred that drivers are distracted cognitively, visually, and manually. The findings have implications for researchers and federal, state, and local agencies who are aware of the consequences of cybersecurity threats for the operation and profitability of the transport network. They, for example, assist transport planners in prioritizing equipment security efforts and resource allocation to the areas of greatest risk, and help to prepare contingency plans based on drivers’ behavioral response.