Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. Only very recently has the question of ‘democratic backsliding’ attracted any research attention. We argue that democratic instability is best understood with tools from complexity science. The explanatory power of complexity science arises from several features of complex systems. Their relevance in the context of democracy is discussed. Several policy recommen- dations are offered to help stabilize current systems of representative democracy.

The idea that democracy is under threat, after being largely dormant for at least 40 years, is looming increasingly large in public discourse. Complex systems theory offers a range of powerful new tools to analyse the stability of social institutions in general, and democracy in particular. What makes a democracy stable? And which processes potentially lead to instability of a democratic system? This paper offers a complex systems perspective on this question, informed by areas of the mathematical, natural, and social (...) sciences. We explain the meaning of the term 'stability' in different disciplines and discuss how laws, rules, and regulations, but also norms, conventions, and expectations are decisive for the stability of a social institution such as democracy. (shrink)

Political scientists have conventionally assumed that achieving democracy is a one-way ratchet. Only very recently has the question of “democratic backsliding” attracted any research attention. We argue that democratic instability is best understood with tools from complexity science. The explanatory power of complexity science arises from several features of complex systems. Their relevance in the context of democracy is discussed. Several policy recommendations are offered to help stabilize current systems of representative democracy.

We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspirational data distributions: data generated to reflect an ideally fair, FEO-satisfying society. FEO states that everyone with the same talent and willingness to use it should have the same chance of achieving advantageous social positions (e.g., employment), regardless of their background circumstances (e.g., socioeconomic status). Satisfying FEO requires alterations to social structures such as school (...) assignments. Our paper describes RAWLSNET, a method which takes as input a BN representation of an FEO application and alters the BN's parameters so as to satisfy FEO when possible, and minimize deviation from FEO otherwise. We also offer guidance for applying RAWLSNET, including on recognizing proper applications of FEO. We demonstrate the use of our system with publicly available data sets. RAWLSNET's altered BNs offer the novel capability of generating aspirational data for FEO-relevant tasks. Aspirational data are free from the biases of real-world data, and thus are useful for recognizing and detecting sources of unfairness in machine learning algorithms besides biased data. (shrink)

I argue that the aesthetic theoretical virtues of beauty, simplicity, and unification, as well as the evidential virtue of explanatory depth, can transform theistic-friendly personal cause (PC) arguments—like the kalām cosmological argument (KCA) and the fine-tuning argument—into stand-alone arguments for monotheism. The aesthetic virtues allow this by providing us with the grounds to rationally accept a perfect personal cause (i.e., God) as the best PC to believe in given the success of some PC argument. Using the KCA as an example, (...) I argue that, once the KCA is accepted and a PC believed in, then a theory that posits a perfect PC as the cause of the universe is more beautiful, simpler, and has more unification and explanatory depth than the imperfect PC normally posited by the KCA’s standard conceptual analysis. And the same would hold true for any imperfect PC. Thus, once a PC argument has been accepted, the perfect PC theory is preferable to hold over any other PC theory. Finally, I address various objections to this reasoning. (shrink)

I argue that the creation and freely chosen salvation and everlasting bliss of even just one person is a greater good than any finite amount of evil and suffering. Since it is extremely likely (if not certain) that, out of all possible individuals that could exist, some (or at least one) would only be freely saved through the contemplation and experience of evil and suffering, then God would be justified in creating a world with evil and suffering to allow for (...) the salvation of such individuals, so long as no one else freely lost their salvation who otherwise would not have lost it because of the evil and suffering. Thus, the problem of evil dissipates, as a world with evil and suffering, even seemingly gratuitous evil and suffering, would be entirely expected given theism. (shrink)

In this paper we investigate some mathematical consequences of the Equivocation Principle, and the Maximum Entropy models arising from that, for first order languages. We study the existence of Maximum Entropy models for these theories in terms of the quantifier complexity of the theory and will investigate some invariance and structural properties of such models.

The argument from divine hiddenness (ADH) requires accepting that nonresistant nonbelief has existed or does exist. Yet some reasons for accepting nonresistant nonbelief are also reasons for accepting theistic-supporting and naturalism-falsifying evidentially compelling religious experiences (ECREs). Additionally, any reasons for rejecting ECREs can be used to reject nonresistant nonbelief, thus creating parity (at the very least) of epistemic warrant between the two claims. Consequently, accepting nonresistant nonbelief should lead to accepting ECREs. Accepting nonresistant nonbelief therefore indirectly threatens naturalism, atheism and (...) even the ADH itself. To any reason that can be given for rejecting ECREs there corresponds a parallel reason for rejecting nonresistant nonbelief. So it is irrational to accept the ADH while refusing to accept ECREs. Yet the existence of ECREs contradicts the ADH’s conclusion. So the ADH is self-defeating. (shrink)

We study the anchoring effect in a computational model of group deliberation on preference rankings. Anchoring is a form of path-dependence through which the opinions of those who speak early have a stronger influence on the outcome of deliberation than the opinions of those who speak later. We show that anchoring can occur even among fully rational agents. We then compare the respective effects of anchoring and three other determinants of the deliberative outcome: the relative weight or social influence of (...) the speakers, the popularity of a given speaker's opinion, and the homogeneity of the group. We find that, on average, anchoring has the strongest effect among these. We finally show that anchoring is often correlated with increases in proximity to single-plateauedness. We conclude that anchoring can constitute a structural bias that might hinder some of the otherwise positive effects of group deliberation. (shrink)

The Evil God (EG) challenge alleges that because arguments used to support belief in a Good God (GG) can be mirrored by the EG hypothesis, then belief in the former is no more reasonable than belief in the latter. Thus, there is an epistemic symmetry between both hypotheses. This paper argues that one of the EG’s secondary traits, specifically his maximal possessiveness, would render it very likely, if not certain, that the EG would _not_ create anything at all. By contrast, (...) the GG would very likely create. Consequently, the existence of anything non-divine would generate a significant asymmetry between the hypotheses—one that favors the GG hypothesis. Moreover, strategies to overcome this argument fail, and as such, the EG challenge is severely undermined. (shrink)

One objection against the kalam is that while the standard arguments for its causal premise apply to things in the universe, they do not apply to the universe itself. Thus, universes could come into existence uncaused from nothing. This objection, however, creates a situation where an absurd universe is as likely to come into existence uncaused as a normal universe is. This then generates serious skepticism about the reliability of our cognitive faculties, the truth of our sensory inputs, and our (...) past knowledge, thus creating a reductio ad absurdum against the objection. (shrink)

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function neural network modeling technique to predict preschool instructors’ technology usage in classes based on (...) recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research. (shrink)

We study probabilistic characterisation of a random model of a finite set of first order axioms. Given a set of first order axioms.

We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty and represent the agent’s beliefs about probability, we consider a plausibility map, associating to each possible distribution a plausibility ranking. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds. We consider two forms of conditioning or belief update, corresponding to the acquisition of two types of information: learning observable evidence obtained by (...) repeated sampling from the unknown distribution; and learning higher-order information about the distribution. The first changes only the plausibility map, but leaves the given set of possible distributions essentially unchanged; the second rules out some distributions, thus shrinking the set of possibilities, without changing their plausibility ordering.. We look at stability of beliefs under either of these types of learning, defining two related notions, as well as a measure of the verisimilitude of a given plausibility model. We prove a number of convergence results, showing how our agent’s beliefs track the true probability after repeated sampling, and how she eventually gains in a sense knowledge of that true probability. Finally, we sketch the contours of a dynamic doxastic logic for statistical learning. (shrink)

Due to digital transformation, technology advancements, telework, we can no longer pretend that traditional work offers high incentives and efficiency, but on the contrary, traditional work falls behind each year, deeming organizations and individuals to adopt the agile work. Rapid technological developments have altered the way businesses operate, with the goal of producing viable solutions in an environment fraught with unpredictability. This paper is a theoretical narrative review on the general topic of agile work. The present paper addresses the issue (...) of determining the best international practices for implementing agile methodology at work, carrying out a theoretical narrative review. Basing on literature on present-day theory the authors make an attempt to explain the implications of implementing agile methodologies into organizational culture, summarize existing approaches to classifying the main effects on implementing agile methodologies at work, consolidate and document best international practices for agile methodologies development among employers. After defining the concepts of agile method tailoring and agile maturity, the data obtained in the theoretical narrative analysis reveal that the implementing agile methodologies have a direct impact on management style, on teams, on learning environments, and on employee’s mental health. An overall conclusions and discussions section is presented along with the personal opinion of authors. This work is intended to open a post-pandemic agile work research methodology, since there are no systematic approaches to this topic. (shrink)

(1997). Individuals confront tradition: Scholars in eleventh‐ and twelfth‐century Europe. The European Legacy: Vol. 2, The Individual in European Culture, pp. 1313-1324.

Throughout its matrix of atoms, myoglobin has a network of cavities that are inhabited for short lengths of time by ligands released by photolysis from the myoglobin heme. The purpose or effect of this cavity network is not clear. A recently published kinetic scheme that fits data from many native and mutant myoglobin oxygen photolysis experiments can be modified easily into a kinetic scheme that includes kinetic proofreading. Proofreading would provide protection against contaminants and, specifically, might help protect the cell (...) from carbon monoxide poisoning. Here we present a two‐part model: (1) myoglobin represented by a kinetic description, which includes proofreading reactions associated with the cavities, and (2) a reaction–diffusion description of a myocyte model in which the part 1 myoglobin acts as a mobile buffer in simultaneous carbon monoxide and oxygen gradients. The non‐equilibrium nature of part 2 should promote the proofreading function of part 1. A simulation using the model demonstrates that the cavity system can in principle proofread, reducing mitochondrial enzyme contamination. BioEssays 26:422–433, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

Pepper & Nettle argue that the more present-oriented behavior associated with a low socioeconomic status is an adaptive response to having relatively little control over the future. However, a study of fasters during Ramadan shows that self-imposed deprivation, which carries no implications regarding the ability to realize deferred rewards, is associated with loss and risk aversion.

According to the objective Bayesian approach to inductive logic, premisses inductively entail a conclusion just when every probability function with maximal entropy, from all those that satisfy the premisses, satisfies the conclusion. When premisses and conclusion are constraints on probabilities of sentences of a first-order predicate language, however, it is by no means obvious how to determine these maximal entropy functions. This paper makes progress on the problem in the following ways. Firstly, we introduce the concept of a limit in (...) entropy and show that, if the set of probability functions satisfying the premisses contains a limit in entropy, then this limit point is unique and is the maximal entropy probability function. Next, we turn to the special case in which the premisses are categorical sentences of the logical language. We show that if the uniform probability function gives the premisses positive probability, then the maximal entropy function can be found by simply conditionalising this uniform prior on the premisses. We generalise our results to demonstrate agreement between the maximal entropy approach and Jeffrey conditionalisation in the case in which there is a single premiss that specifies the probability of a sentence of the language. We show that, after learning such a premiss, certain inferences are preserved, namely inferences to inductive tautologies. Finally, we consider potential pathologies of the approach: we explore the extent to which the maximal entropy approach is invariant under permutations of the constants of the language, and we discuss some cases in which there is no maximal entropy probability function. (shrink)

There are various ways to reach a group decision on a factual yes–no question. One way is to vote and decide what the majority votes for. This procedure receives some epistemological support from the Condorcet Jury Theorem. Alternatively, the group members may prefer to deliberate and will eventually reach a decision that everybody endorses—a consensus. While the latter procedure has the advantage that it makes everybody happy, it has the disadvantage that it is difficult to implement, especially for larger groups. (...) Besides, the resulting consensus may be far away from the truth. And so we ask: Is deliberation truth-conducive in the sense that majority voting is? To address this question, we construct a highly idealized model of a particular deliberation process, inspired by the movie Twelve Angry Men, and show that the answer is ‘yes’. Deliberation procedures can be truth-conducive just as the voting procedure is. We then explore, again on the basis of our model and using agent-based simulations, under which conditions it is better epistemically to deliberate than to vote. Our analysis shows that there are contexts in which deliberation is epistemically preferable and we will provide reasons for why this is so. (shrink)

Qushayrī, ʻAbd al-Karīm ibn Hawāzin, 986-1072; views on; ethics; soul; Islam.

Normal 0 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} Review of Sandu Frunză , God and the Holocaust in Elie Wiesel´s work. An ethics of responsibility (Dumnezeu și Holocaustul la Elie Wiesel. O etică a responsabilității), (Contemporanul Publishing House, Bucharest, 2010).