This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. (...) The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN’s ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool. (shrink)

In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, and 12 (...) statistical features, including maximum value, mean value, and linear slope. A total of 384-dimensional statistical features was extracted and compared with the classification ability of different emotional features. The deficiencies of the traditional classification algorithm are first studied, and then by introducing confusion, constructing multilevel classifiers, and tuning each level of the classifier, better recognition rates than traditional primary classification are obtained. This paper introduces label information for supervised training to further improve the features of multifunctional fusion music. Experiments show that this information has excellent performance in multifunctional fusion music recognition. The experiments compare the multilevel classifier with primary classification, and the multilevel classification with the primary classification and the classification performance is improved, and the recognition rate of the multilevel classification algorithm is also improved over the multilevel classification algorithm, proving that the excellent performance with multiple levels of classification. (shrink)

This paper considers how surprise (or its lack) can be cast as a metacognitive signal with an adaptive function in learning new knowledge and revising belief networks. It reviews the phenomena that may hinder this signal (e.g., hindsight bias) and argues for its extrinsic exploitation in instructional and educational contexts by educators, journalists and parents, who might train learners to internalize the use of surprise to drive explanation‐based learning.

Chris Meek. Strong-Completeness and Faithfulness in Belief Networks.

Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the (...) overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies. (shrink)

We construct a probabilistic coherence measure for information sets which determines a partial coherence ordering. This measure is applied in constructing a criterion for expanding our beliefs in the face of new information. A number of idealizations are being made which can be relaxed by an appeal to Bayesian Networks.

People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures. Finally, they rated (...) the accuracy of the statements again. The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also find that conversational content predicts belief change from pre-test to post-test. (shrink)

Metaphors of positions, spectrums, perspectives, viewpoints, and polarization reflect the same model, which treats beliefs—and the people who hold them—as points in space. This model is deeply rooted in quantitative research methods and influential traditions of Continental philosophy, and it is evident in some qualitative research. It can suggest that deliberation is difficult and rare because many people are located far apart ideologically, and their respective positions can be explained as dependent variables of factors like personality, partisanship, and demographics. An (...) alternative model treats a given person’s beliefs as connected by reasons to form networks. People disclose the connections among their respective beliefs when they discuss issues. This model offers insights about specific cases, such as discussions conducted on two US college campuses, which are represented here as belief-networks. The model also supports a more optimistic view of the public’s capacity to deliberate. (shrink)

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources (...) correlated with positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

By assessing interrelationships among variables within a specified theoretical framework, network analysis provides nuanced insights into how associations between psychological constructs are related to outcome measures. Noting this, the authors used NA to examine connections between Paranormal Belief, cognitive-perceptual factors, and well-being. Data derived from a sample of 3,090 participants who completed standardised self-report measures capturing the study constructs online. Transliminality, Unusual Experiences, and Depressive Experience demonstrated high expected influence centrality. This indicated that these factors were the most strongly (...) connected and influential in the network. Moreover, Transliminality was a connecting variable between Paranormal Belief, positive schizotypy, and psychopathology. Depressive Experience bridged the relationship between Transliminality and well-being. The conceptual implications of these outcomes are discussed with regards to better understanding relationships between Paranormal Belief, cognitive-perceptual factors, and well-being. (shrink)

Belief polarization is said to occur when two people respond to the same evidence by updating their beliefs in opposite directions. This response is considered to be “irrational” because it involves contrary updating, a form of belief updating that appears to violate normatively optimal responding, as for example dictated by Bayes' theorem. In light of much evidence that people are capable of normatively optimal behavior, belief polarization presents a puzzling exception. We show that Bayesian networks, or (...) Bayes nets, can simulate rational belief updating. When fit to experimental data, Bayes nets can help identify the factors that contribute to polarization. We present a study into belief updating concerning the reality of climate change in response to information about the scientific consensus on anthropogenic global warming. The study used representative samples of Australian and U.S. participants. Among Australians, consensus information partially neutralized the influence of worldview, with free-market supporters showing a greater increase in acceptance of human-caused global warming relative to free-market opponents. In contrast, while consensus information overall had a positive effect on perceived consensus among U.S. participants, there was a reduction in perceived consensus and acceptance of human-caused global warming for strong supporters of unregulated free markets. Fitting a Bayes net model to the data indicated that under a Bayesian framework, free-market support is a significant driver of beliefs about climate change and trust in climate scientists. Further, active distrust of climate scientists among a small number of U.S. conservatives drives contrary updating in response to consensus information among this particular group. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to (...) incorporate ‘trickled in’ elements of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Several philosophers and psychologists have characterized belief in conspiracy theories as a product of irrational reasoning. Proponents of conspiracy theories apparently resist revising their beliefs given disconfirming evidence and tend to believe in more than one conspiracy, even when the relevant beliefs are mutually inconsistent. In this paper, we bring leading views on conspiracy theoretic beliefs closer together by exploring their rationality under a probabilistic framework. We question the claim that the irrationality of conspiracy theoretic beliefs stems from an (...) inadequate response to disconfirming evidence and internal incoherence. Drawing analogies to Lakatosian research programs, we argue that maintaining a core conspiracy belief can be Bayes-rational when it is embedded in a network of auxiliary beliefs, which can be revised to protect the more central belief from disconfirmation. We propose that the (ir)rationality associated with conspiracy belief lies not in a flawed updating method, but in their failure to converge toward well-confirmed, stable belief networks in the long run. This approach not only reconciles previously disjointed views, but also points toward more specific hypotheses explaining why some agents may be prone to adopting beliefs in conspiracy theories. (shrink)

Several philosophers and psychologists have characterized belief in conspiracy theories as a product of irrational reasoning. Proponents of conspiracy theories apparently resist revising their beliefs given disconfirming evidence and tend to believe in more than one conspiracy, even when the relevant beliefs are mutually inconsistent. In this paper, we bring leading views on conspiracy theoretic beliefs closer together by exploring their rationality under a probabilistic framework. We question the claim that the irrationality of conspiracy theoretic beliefs stems from an (...) inadequate response to disconfirming evidence and internal incoherence. Drawing analogies to Lakatosian research programs, we argue that maintaining a core conspiracy belief can be Bayes-rational when it is embedded in a network of auxiliary beliefs, which can be revised to protect the more central belief from disconfirmation. We propose that the irrationality associated with conspiracy belief lies not in a flawed updating method, but in a failure to converge toward wellconfirmed, stable belief networks in the long run. This approach not only reconciles previously disjointed views, but also points toward more specific descriptions of why agents may be prone to adopting beliefs in conspiracy theories. (shrink)

What beliefs are, what they do for us, how we come to hold them, and how to evaluate them. Our beliefs constitute a large part of our knowledge of the world. We have beliefs about objects, about culture, about the past, and about the future. We have beliefs about other people, and we believe that they have beliefs as well. We use beliefs to predict, to explain, to create, to console, to entertain. Some of our beliefs we call theories, and (...) we are extraordinarily creative at constructing them. Theories of quantum mechanics, evolution, and relativity are examples. But so are theories about astrology, alien abduction, guardian angels, and reincarnation. All are products (with varying degrees of credibility) of fertile minds trying to find explanations for observed phenomena. In this book, Nils Nilsson examines beliefs: what they do for us, how we come to hold them, and how to evaluate them. We should evaluate our beliefs carefully, Nilsson points out, because they influence so many of our actions and decisions. Some of our beliefs are more strongly held than others, but all should be considered tentative and changeable. Nilsson shows that beliefs can be quantified by probability, and he describes networks of beliefs in which the probabilities of some beliefs affect the probabilities of others. He argues that we can evaluate our beliefs by adapting some of the practices of the scientific method and by consulting expert opinion. And he warns us about “belief traps”—holding onto beliefs that wouldn't survive critical evaluation. The best way to escape belief traps, he writes, is to expose our beliefs to the reasoned criticism of others. (shrink)

The bounded confidence model has become a popular tool for studying communities of epistemically interacting agents. The model makes the idealizing assumption that all agents always have access to all other agents’ belief states. We draw on resources from network epistemology to do away with this assumption. In the model to be proposed, we impose an explicit communication network on a community, due to which each agent has access to the beliefs of only a selection of other agents. A (...) much-discussed result from network epistemology shows that densely connected communication networks are not always preferable to sparser networks. The aim of this paper is to investigate whether there are any noteworthy network effects in a version of the bounded confidence model augmented with communication networks, and in particular whether the aforementioned result from network epistemology can be replicated in that version. (shrink)

Does connectionism spell doom for folk psychology? I examine the proposal that cognitive representational states such as beliefs can play no role if connectionist models - - interpreted as radical new cognitive theories -- take hold and replace other cognitive theories. Though I accept that connectionist theories are radical theories that shed light on cognition, I reject the conclusion that neural networks do not represent. Indeed, I argue that neural networks may actually give us a better working notion (...) of cognitive representational states such as beliefs, and in so doing give us a better understanding of how these states might be instantiated in neural wetware. (shrink)

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent‐based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of (...) specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long‐held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well‐connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation. (shrink)

We explore psychological contracts as mechanisms by which individuals gain influence in organizations. Using two distinct research settings and longitudinal analysis, we demonstrate that ideological contracts endow individuals with increased centrality in the organization’s influence network. More generally, we propose that an important outcome of different psychological contract types may be how they affect the nature of influence in organizations.

This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems for natural-language representation and reasoning systems, because--unlike pure indicators--they cannot be replaced by coreferential NPs without changing the meaning of (...) the embedding sentence. Therefore, the referent of the quasi-indicator must be represented in such a way that no invalid coreferential claims are entailed. The importance of quasi-indicators is discussed, and it is shown that all four of the above categories of belief reports can be handled by a single representational technique using belief spaces containing intensional entities. Inference rules and belief-revision techniques for the system are also examined. (shrink)

Two models are developed for updating opinions in social networks under situations where certain beliefs might be considered to be competing. These two models represent different attitudes of people towards the perceived conflict between beliefs. In both models agents have a degree of tolerance, which represents the extent to which the agent takes into account the differing beliefs of other agents, and a degree of conflict, which represents the extent to which two beliefs are considered to be competing. Computer (...) simulations are used to determine how the opinion dynamics are affected by the inclusion of conflict. Results show that conflict can enhance the formation of consensus within the network in certain circumstances according to one of the models. (shrink)

Two models are developed for updating opinions in social networks under situations where certain beliefs might be considered to be competing. These two models represent different attitudes of people towards the perceived conflict between beliefs. In both models agents have a degree of tolerance, which represents the extent to which the agent takes into account the differing beliefs of other agents, and a degree of conflict, which represents the extent to which two beliefs are considered to be competing. Computer (...) simulations are used to determine how the opinion dynamics are affected by the inclusion of conflict. Results show that conflict can enhance the formation of consensus within the network in certain circumstances according to one of the models. (shrink)

The convergence of increasingly efficient high throughput sequencing technology and ubiquitous Internet use by the public has fueled the proliferation of companies that provide personal genetic information (PGI) direct-to-consumers. Companies such as 23andme (Mountain View, CA) and Navigenics (Foster City, CA) are emblematic of a growing market for PGI that some argue represents a paradigm shift in how the public values this information and incorporates it into how they behave and plan for their futures. This new class of social networking (...) business ventures that market the science of the personal genome illustrates the new trend in collaborative science. In addition to fostering a consumer empowerment movement, it promotes the trend of democratizing information—openly sharing of data with all interested parties, not just the biomedical researcher—for the purposes of pooling data (increasing statistical power) and escalating the innovation process. This target article discusses the need for new approaches to studying DTC genomics using social network analysis to identify the impact of obtaining, sharing, and using PGI. As a locus of biosociality, DTC personal genomics forges social relationships based on beliefs of common genetic susceptibility that links risk, disease, and group identity. Ethical issues related to the reframing of DTC personal genomic consumers as advocates and research subjects and the creation of new social formations around health research may be identified through social network analysis. (shrink)

There is widespread belief that connectionist networks are dramatically different from classical or symbolic models. However, connectionists rarely test this belief by interpreting the internal structure of their nets. A new approach to interpreting networks was recently introduced by Berkeley et al. (1995). The current paper examines two implications of applying this method: (1) that the internal structure of a connectionist network can have a very classical appearance, and (2) that this interpretation can provide a cognitive (...) theory that cannot be dismissed as a mere implementation. (shrink)

The ordinary attribution of intentionality to (nonhuman) animals raises serious problems for fashionable linguistic accounts of belief and of intentionality generally; and many of the alleged problems arise from such linguistic theories of mind. Another deeper source of alleged problems is the apartness thesis, that there is a significant difference in kind, with substantial moral import, between humans and other animals; for the last lines of defence of this erroneous thesis consist in making out that there are significant intentional (...) differences. A wide range of recent arguments against assigning intentionality (in the full sense) to animals are criticized in detail: those of Stich and Williams, in terms of animals lacking effective or specifiable concepts (concepts now replacing souls); those of Stich and Davidson based on the requirement for beliefs of an isomorphic belief network; those based on the usual opacity of intentionality; those of Descartes and Davidson and others based on the requirement of, or arguments to the essentiality of, language use for attributions of intentionality; arguments based on the requirement of capacity for pretence or awareness of error; and arguments used by Vendler and Malcolm. Several different arguments for assigning intentionality to animals are then advanced, arguments from cerebral organization, exteriorization arguments, and interiorization arguments from the semantical analysis of intentionality. The main arguments advanced are not analogical; they are not anthropocentric, or the result of personifying languageless animals; and the attributions of intentionality they lead to are not impoverished or of reduced status. (shrink)

Ethical networks are an emerging form of social alliance based on collaboration between organizations that share a common ethical commitment. Grounded in a theoretical framework of virtue-based business ethics and focusing on nonprofit alliances, this study investigates the virtuousness of ethical networks; that is, how they trigger virtuous practices in their member nonprofit organizations. Adopting a qualitative grounded theory approach, the study focuses on one of the largest Italian ethical networks of nonprofit organizations operating in the social (...) care sector. The findings show that shared ethical values and religious beliefs are positively associated with ethical network building. Based on these findings, a circular model of virtuousness is proposed in which ethical networks foster virtuous practices among their members at four levels: (1) the strategic orientation level, (2) the institutional level, (3) the organizational level, and (4) the relational level. At each of these levels, ethical networks foster a habituation to virtues and the propagation of virtuous behaviors among their members. Theoretical, practical, and social implications of the research findings are discussed. (shrink)