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Just a few years is all it took for the debt crisis to bring down the mighty 'twin towers' of American and European capitalism and undo two centuries of Western dominance on the world's economic and political stage. Daniel Pinto offers a unique insight into how the East is winning the battle for economic supremacy, thereby shaping the new world order and leaving America and Europe with no choice but to reinvent themselves. Drawing on his own experience at the highest levels of business and finance, Pinto dismisses the common notion that globalisation is to blame for anaemic growth, massive une

AbstractPhilosophers of science have argued that epistemic diversity is an asset for the production of scientific knowledge, guarding against the effects of biases, among other advantages. The growing privatization of scientific research, on the contrary, has raised important concerns for philosophers of science, especially with respect to the growing sources of biases in research that it seems to promote. Recently, Holman and Bruner (2017) have shown, using a modified version of Zollman (2010) social network model, that an industrial selection bias can emerge in a scientific community, without corrupting any individual scientist, if the community is epistemically diverse. In this paper, we examine the strength of industrial selection using a reinforcement learning model, which simulates the process of industrial decision-making when allocating funding to scientific projects. Contrary to Holman and Bruner's model, in which the probability of success of the agents when performing an action is given a priori, in our model the industry learns about the success rate of individual scientists and updates the probability of success on each round. The results of our simulations show that even without previous knowledge of the probability of success of an individual scientist, the industry is still able to disrupt scientific consensus. In fact, the more epistemically diverse the scientific community, the easier it is for the industry to move scientific consensus to the opposite conclusion. Interestingly, our model also shows that having a random funding agent seems to effectively counteract industrial selection bias. Accordingly, we consider the random allocation of funding for research projects as a strategy to counteract industrial selection bias, avoiding commercial exploitation of epistemically diverse communities.

Weisberg and Muldoon's epistemic landscape model (ELM) has been one of the most significant contributions to the use of agent-based models in philosophy. The model provides an innovative approach to establishing the optimal distribution of cognitive labor in scientific communities, using an epistemic landscape. In the paper, we provide a critical examination of ELM. First, we show that the computing mechanism for ELM is correct insofar as we are able to replicate the results using another programming language. Second, we show that small changes in the rules that determine the behavior of individual agents can lead to important changes in simulation results. Accordingly, we claim that ELM results are robust with respect to the computing mechanism, but not necessarily across parameter space. We conclude by reflecting on the possible lessons to be gained from ELM as a class of simulations or cluster of models.

Highlights Cantabrian capercaillie population has recently been classified as "Critically Endangered" by Spanish Government. To develop management plans, information on demographic parameters are necessary to understand population dynamics. In 2019 we estimated the size of population at 191 individuals. Since the 1970s, we estimated a shrinkage of the population range by 83%. Apparent annual survival was estimated at 0.707 and per-capita recruitment at 0.233. Abstract The capercaillie Tetrao urogallus - the world's largest grouse - is a circumboreal forest species, which only two remaining populations in Spain: one in the Cantabrian mountains in the west and the other in the Pyrenees further east. Both have shown severe declines, especially in the Cantabrian population, which has recently been classified as "Critically Endangered". To develop management plans, information on demographic parameters is necessary to understand and forecast population dynamics. We used spatial capture-recapture (SCR) modeling and non-invasive DNA samples to estimate the current population size in the whole Cantabrian mountain range. In addition, for the assessment of population status, we analyzed the population trajectory over the last 42 years (1978–2019) at 196 leks on the Southern slope of the range, using an integrated population model with a Dail-Madsen model at its core, combined with a multistate capture-recapture model for survival and a Poisson regression for productivity. For 2019, we estimate the size of the entire population at 191 individuals (95% BCI 165–222) for an estimated 60 (48–78) females and 131 (109–157) males. Since the 1970s, our study estimates a shrinkage of the population range by 83%. The population at the studied leks in 2019 was at about 10% of the size estimated for 1978. Apparent annual survival was estimated at 0.707 (0.677–0.735), and per-capita recruitment at 0.233 (0.207–0.262), and insufficient to maintain a stable population. We suggest work to improve the recruitment (and survival) and manage ...