It Could Not Be Seen Because It Could Not Be Believed on June 30, 2013

Nineteen Prescott Fire Department, Granite Mountain Hot Shot (GMHS) wildland firefighters (WF) perished in Arizona in June 2013 Yarnell Hill Fire, an inexplicable wildland fire disaster. In complex wildland fires, sudden, dynamic changes in human factors and fire conditions can occur, thus mistakes can be unfortunately fatal. Individual and organizational faults regarding the predictable, puzzling, human failures that will result in future WF deaths are addressed. The GMHS were individually, then collectively fixated with abandoning their Safety Zone to reengage, committing themselves at the worst possible time, to relocate to another Safety Zone - a form of collective tunnel vision. Our goal is to provoke meaningful discussion toward improved wildland firefighter safety with practical solutions derived from a long-established wildland firefighter expertise/performance in a fatality-prone profession. Wildfire fatalities are unavoidable, hence these proposals, applied to ongoing training, can significantly contribute to other well-thought-out and validated measures to reduce them

Divergent Effects of Beliefs in Heaven and Hell on National Crime Rates

Though religion has been shown to have generally positive effects on normative ‘prosocial’ behavior, recent laboratory research suggests that these effects may be driven primarily by supernatural punishment. Supernatural benevolence, on the other hand, may actually be associated with less prosocial behavior. Here, we investigate these effects at the societal level, showing that the proportion of people who believe in hell negatively predicts national crime rates whereas belief in heaven predicts higher crime rates. These effects remain after accounting for a host of covariates, and ultimately prove stronger predictors of national crime rates than economic variables such as GDP and income inequality. Expanding on laboratory research on religious prosociality, this is the first study to tie religious beliefs to large-scale cross-national trends in pro- and anti-social behavior

Fortune Favours the Bold: An Agent-Based Model Reveals Adaptive Advantages of Overconfidence in War

Overconfidence has long been considered a cause of war. Like other decision-making biases, overconfidence seems detrimental because it increases the frequency and costs of fighting. However, evolutionary biologists have proposed that overconfidence may also confer adaptive advantages: increasing ambition, resolve, persistence, bluffing opponents, and winning net payoffs from risky opportunities despite occasional failures. We report the results of an agent-based model of inter-state conflict, which allows us to evaluate the performance of different strategies in competition with each other. Counter-intuitively, we find that overconfident states predominate in the population at the expense of unbiased or underconfident states. Overconfident states win because: (1) they are more likely to accumulate resources from frequent attempts at conquest; (2) they are more likely to gang up on weak states, forcing victims to split their defences; and (3) when the decision threshold for attacking requires an overwhelming asymmetry of power, unbiased and underconfident states shirk many conflicts they are actually likely to win. These “adaptive advantages” of overconfidence may, via selection effects, learning, or evolved psychology, have spread and become entrenched among modern states, organizations and decision-makers. This would help to explain the frequent association of overconfidence and war, even if it no longer brings benefits today

Effort Perception is Made More Accurate with More Effort and When Cooperating with Slackers

Recent research on the conditions that facilitate cooperation is limited by a factor that has yet to be established: the accuracy of effort perception. Accuracy matters because the fitness of cooperative strategies depends not just on being able to perceive others' effort but to perceive their true effort. In an experiment using a novel effort-tracker methodology, we calculate the accuracy of human effort perceptions and show that accuracy is boosted by more absolute effort (regardless of relative effort) and when cooperating with a "slacker" rather than an "altruist". A formal model shows how such an effort-prober strategy is likely to be an adaptive solution because it gives would-be collaborators information on when to abort ventures that are not in their interest and opt for ones that are. This serves as a precautionary measure against systematic exploitation by extortionist strategies and a descent into uncooperativeness. As such, it is likely that humans have a bias to minimize mistakes in effort perception that would commit them to a disadvantageous effort-reward relationship. Overall we find support for the idea that humans have evolved smart effort detection systems that are made more accurate by those contexts most relevant for cooperative tasks

Overconfidence, incentives and digit ratio

This paper contributes to a better understanding of the biological underpinnings of overconfidence by analyzing performance predictions in the Cognitive Reflection Test with and without monetary incentives. In line with the existing literature we find that the participants are too optimistic about their performance on average; incentives lead to higher performance; and males score higher than females on this particular task. The novelty of this paper is an analysis of the relation between participants’ performance prediction accuracy and their second to fourth digit ratio. It has been reported that the digit ratio is a negatively correlated bio-marker of prenatal testosterone exposure. In the un-incentivized treatment, we find that males with low digit ratios, on average, are significantly more overconfident about their performance. In the incentivized treatment, however, we observe that males with low digit ratios, on average, are less overconfident about their performance. These effects are not observed in females. We discuss how these findings fit into the literature on testosterone and decision making and how they might help to explain seemingly opposing evidence

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background. Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling

Synergy between intention recognition and commitments in cooperation dilemmas

Commitments have been shown to promote cooperation if, on the one hand, they can be sufficiently enforced, and on the other hand, the cost of arranging them is justified with respect to the benefits of cooperation. When either of these constraints is not met it leads to the prevalence of commitment free-riders, such as those who commit only when someone else pays to arrange the commitments. Here, we show how intention recognition may circumvent such weakness of costly commitments. We describe an evolutionary model, in the context of the one-shot Prisoner's Dilemma, showing that if players first predict the intentions of their co-player and propose a commitment only when they are not confident enough about their prediction, the chances of reaching mutual cooperation are largely enhanced. We find that an advantageous synergy between intention recognition and costly commitments depends strongly on the confidence and accuracy of intention recognition. In general, we observe an intermediate level of confidence threshold leading to the highest evolutionary advantage, showing that neither unconditional use of commitment nor intention recognition can perform optimally. Rather, our results show that arranging commitments is not always desirable, but that they may be also unavoidable depending on the strength of the dilemma.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Beliefs about bad people are volatile

People form moral impressions rapidly, effortlessly and from a remarkably young age1,2,3,4,5. Putatively \u2018bad\u2019 agents command more attention and are identified more quickly and accurately than benign or friendly agents5,6,7,8,9,10,11,12. Such vigilance is adaptive, but can also be costly in environments where people sometimes make mistakes, because incorrectly attributing bad character to good people damages existing relationships and discourages forming new relationships13,14,15,16. The ability to accurately infer the moral character of others is critical for healthy social functioning, but the computational processes that support this ability are not well understood. Here, we show that moral inference is explained by an asymmetric Bayesian updating mechanism in which beliefs about the morality of bad agents are more uncertain (and therefore more volatile) than beliefs about the morality of good agents. This asymmetry seems to be a property of learning about immoral agents in general, as we also find greater uncertainty for beliefs about the non-moral traits of bad agents. Our model and data reveal a cognitive mechanism that permits flexible updating of beliefs about potentially threatening others, a mechanism that could facilitate forgiveness when initial bad impressions turn out to be inaccurate. Our findings suggest that negative moral impressions destabilize beliefs about others, promoting cognitive flexibility in the service of cooperative but cautious behaviour

A multi-metric approach to investigate the effects of weather conditions on the demographic of a terrestrial mammal, the European badger (Meles meles)

Models capturing the full effects of weather conditions on animal populations are scarce. Here we decompose yearly temperature and rainfall into mean trends, yearly amplitude of change and residual variation, using daily records. We establish from multi-model inference procedures, based on 1125 life histories (from 1987 to 2008), that European badger (Meles meles) annual mortality and recruitment rates respond to changes in mean trends and to variability in proximate weather components. Variation in mean rainfall was by far the most influential predictor in our analysis. Juvenile survival and recruitment rates were highest at intermediate levels of mean rainfall, whereas low adult survival rates were associated with only the driest, and not the wettest, years. Both juvenile and adult survival rates also exhibited a range of tolerance for residual standard deviation around daily predicted temperature values, beyond which survival rates declined. Life-history parameters, annual routines and adaptive behavioural responses, which define the badgers’ climatic niche, thus appear to be predicated upon a bounded range of climatic conditions, which support optimal survival and recruitment dynamics. That variability in weather conditions is influential, in combination with mean climatic trends, on the vital rates of a generalist, wide ranging and K-selected medium-sized carnivore, has major implications for evolutionary ecology and conservation