The Future of the Global Economy in the Light of Inflationary and Deflationary Trends and Long Cycles Theory

Recent years and months have evidenced an increase in deflationary phenomena. The present article defines the reasons for the problem, explains the irregularity of the inflation–deflation processes in the world and forecasts on this basis that the crisis-depressive phase of development in the global economy will continue for a relatively long time. Based on an analysis of available resources and the theory of long cycles, we believe that in the next 5–10 years, the global economy will continue being in the crisis-depression phase with rather sluggish and weak rises. The article also offers some forecasts for the forthcoming sixth Kondratieff wave (2020–the 2060/70s), identifies its possible technological basis, and discusses possible consequences of the forthcoming technological transformations.     

A gradual reformation: empirical character and causal powers in Kant

According to Kant each person has an empirical character, which is ultimately grounded in one’s free choice. The popular Causal Laws interpretation of empirical character holds that it consists of the causal laws governing our psychology. I argue that this reading has difficulties explaining moral change, the ‘gradual reformation’ of our empirical character: Causal laws cannot change and hence cannot be gradually reformed. I propose an alternative Causal Powers interpretation of empirical character, where our empirical character consists of our mind’s causal powers. The resulting picture of empirical character allows for moral change and Kantian weakness of will.     

Sufficiency and Necessity Assumptions in Causal Structure Induction

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain‐general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.     

Adaptive non-interventional heuristics for covariation detection in causal induction: model comparison and rational analysis

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model.     

Trained interpretive bias and anxiety

The relationship between anxiety and interpretive bias has been studied extensively, but the causal direction of this relationship remains largely unexplored. Do negative interpretations cause anxiety or is anxiety the cause of negative interpretations? Or are the two mutually reinforcing? The present study addressed this issue by experimentally inducing either a negative or a positive interpretive bias using Mathews and Mackintosh' [(2002). Induced emotional interpretation bias and anxiety. Journal of Abnormal Psychology, 109, 604-615] training paradigm and then examining its impact on state anxiety and anxiety vulnerability. In addition, it was investigated as to whether the interpretive bias was trained implicitly. Results indicated that style of interpreting could be manipulated. That is, when confronted with ambiguous information after the training, participants (n=118) interpreted this information congruent with their (positive or negative) training condition. Data on the issue of implicitness showed that participants tended to be explicitly aware of the valence of their training stimuli. Effects of trained interpretive bias on anxiety were only marginal and absent on anxiety vulnerability. It appears that interpretive bias can be trained reliably, but its effects on mood and vulnerability require further explanation.     

The role of mechanism and covariation information in causal belief updating

The present study is aimed at identifying how prior causal beliefs and covariation information contribute to belief updating when evidence, either compatible or contradictory with those beliefs, is provided. Participants were presented with a cover story with which it was intended to activate or generate a causal belief. Variables related to the prior belief (the type of information, the strength of the cause-effect causal link, and how confident the participant was that the link existed) were assessed. Subsequently, participants were presented with covariational information and were asked to update their beliefs in light of the new evidence. Information reliability, prior belief's causal influence magnitude, and the cause-effect level of contingency portrayed by the new information--but not the type of the prior belief--are shown to directly determine belief updating.     

Do chimpanzees know what others can and cannot do? Reasoning about ‘capability’

Much recent comparative work has been devoted to exploring what nonhuman primates understand about physical causality. However, few laboratory experiments have attempted to test what nonhumans understand about what physical acts others are capable of performing. We tested seven chimpanzees’ ability to predict which of two human experimenters could deliver a tray containing a food reward. In the ‘floor’ condition, legs were required to push the tray toward the subject. In the ‘lap’ condition, arms were required to hand the tray to the subject. In Exp. 1, chimpanzees begged (by gesturing) to either an experimenter whose legs were not visible (LNV) or whose arms were not visible (ANV). Rather than flexibly altering their preferences between conditions, the chimpanzees preferred the ANV experimenter regardless of the task. In subsequent experiments, we manipulated various factors that might have controlled the chimpanzees’ preferences, such as (a) distance between experimenter and subject (Experiment 2), (b) amount of occlusion of experimenters’ body (Experiments 2 and 3), (c) contact with the food tray (Experiments 3 and 4) and (d) positioning of barriers that either impeded the movement of the limbs or not (Experiment 5). The chimpanzees’ performance was best explained by attention to cues such as perceived proximity, contact, and maximal occlusion of body that although highly predictive in certain tasks, were irrelevant in others. When the discriminative role of such cues was eliminated, performance fell to chance levels, indicating that chimpanzees do not spontaneously (or after considerable training) use limb visibility as a cue to predict the ability of a human to perform particular physical tasks. Thus, the current findings suggest a possible failure of causal reasoning in the context of reasoning about the use of the limbs to perform physical acts.     

Science and God: An automatic opposition between ultimate explanations

Science and religion have come into conflict repeatedly throughout history, and one simple reason for this is the two offer competing explanations for many of the same phenomena. We present evidence that the conflict between these two concepts can occur automatically, such that increasing the perceived value of one decreases the automatic evaluation of the other. In Experiment 1, scientific theories described as poor explanations decreased automatic evaluations of science, but simultaneously increased automatic evaluations of God. In Experiment 2, using God as an explanation increased automatic evaluations of God, but decreased automatic evaluations of science. Religion and science both have the potential to be ultimate explanations, and these findings suggest that this competition for explanatory space can create an automatic opposition in evaluations.     

The power of causal beliefs and conflicting evidence on causal judgments and decision making

In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. Participants had to decide which of two alternatives would attain a higher outcome on the basis of four cues. After completing the decision task, they were asked to estimate to what extent each cue was a reliable cause of the outcome. Participants were provided with instructions that causally related two of the cues to the outcome, whereas they received neutral information about the other two cues. Two of the four cues—a causal and a neutral cue—had high validity and were both generative. The remaining two cues had low validity, and were generative in Experiment 1, but almost not related to the outcome in Experiment 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training was provided. Results revealed that the impact of causal beliefs and empirical evidence depends on both the experienced pre-training and cue validity. When all cues were generative and participants received pre-training with causal cues, they mostly relied on their causal beliefs, whereas they relied on empirical evidence when they received pre-training with neutral cues. In contrast, when some of the cues were almost not related to the outcome, participants’ responses were primarily influenced by validity and—to a lesser extent—by causal beliefs. In either case, however, the influence of causal beliefs was higher in causal judgments than in decision making. While current theoretical approaches in causal learning focus either on the effect of causal beliefs or empirical evidence, the present research shows that both factors are required to explain the flexibility involved in human inferences.