Anchoring does not activate examples associated with the anchor value

In the standard anchoring paradigm, people first compare a selected attribute of a target to a numeric value—an anchor. A subsequent absolute judgment of the target's attribute value is biased in the direction of the anchor. A prominent theory of the anchoring effect, the selective accessibility model, argues that people make the initial comparison by focusing on similarities between the target and the anchor, which activates information compatible with the anchor value being the target value. This activated information biases the subsequent estimate of the target value. To test the selective activation of information, the present study asked people to provide an example of the target's category following its comparison with an anchor. The attribute values of the provided examples were not biased in the direction of the anchor. However, they were positively associated with estimates of the target value. The study thus provides evidence for the use of activated information in the absolute judgment in the standard anchoring paradigm, but it does not show the selective activation of information compatible with the anchor value predicted by the selective accessibility model.

Applying spatial mutual information to AIS data

This paper examines a new interpretation for spatial mutual information based on the mutual information between an attribute value and a spatial random variable. This new interpretation permits the measurement of variations in spatial mutual information over the domain, not only answering the question of whether a spatial dependency exists and the strength of that dependency, but also allowing the identification of where such dependencies exist. Using simulated and real vessel reporting data, the properties of this new interpretation of spatial mutual information are explored. The utility of the technique in detecting spatial boundaries between regions of data having different statistical properties is examined. The technique is shown to successfully identify vessel traffic boundaries, crossing points between traffic lanes, and transitions between regions having differing vessel movement patterns.

Who is a good journalist? Evaluations of journalistic worth in the era of social media

In this article, I enquire into the ways that journalists understand their identities and values now that social media dominate the routines of networked newsrooms. My approach is grounded on a Discourse Theory framework within which journalism emerges as a symbolic practice constituted through the discourse of its practitioners. Drawing additionally on pragmatic sociology, I understand journalists as reflexive practitioners who discursively attribute value to various orders of worth in order to evaluate their own identities. Taking the British news organisation The Guardian as my case study, my analysis of 10 newsroom interviews demonstrates how journalists develop a series of evaluations in order to identify themselves. My findings confirm a shift in the ways that journalists evaluate themselves, which is today associated with a new valorisation of networking. This shift towards networking, however, does not destroy long-standing journalistic values. It is ultimately their institutional identities that journalists re-invent through social media, and it is according to their institutional expertise that they evaluate themselves as professionals. In conclusion, I argue that, whilst journalists reaffirm their disdain for the financial rewards of the market, by embracing social media networking they expose themselves to the influence of capitalist markets.

Seasonal Heavy Rain Forecasting Method

In this study, we study the technique for predicting heavy / non-rain rainfall after 6 hours from the present using the values of the weather attributes. Through this study, we investigated whether each attribute value is influenced by a specific pattern of weather maps representing heavy and non-heavy rains or seasonally when making heavy / non-heavy forecasts. For the experiment, a 20-year cumulative weather map was learned with Support Vector Machine (SVM) and tested using a set of correct answers for heavy rain and heavy rain. As a result of the experiment, it was found that the heavy rain prediction of SVM showed an accuracy rate of up to 70%, and that it was seasonal variation rather than a specific pattern that influenced the prediction.

Using Decision Analysis to Integrate Habitat and Community Values for Coastal Resilience Planning

Decision analysis is applied to habitat and community resilience planning in Maryland, USA. Sea level rise is causing wetland loss and increased flooding in coastal areas. A team at The Nature Conservancy analyzed a decision to identify high-value conservation planning units across Maryland’s Lower Eastern Shore. The team selected two fundamental objectives: minimize habitat loss and minimize community flood impacts. Sub-objectives included habitat function, habitat migration potential, critical infrastructure, and social vulnerability. Spatial attributes were selected based on ecological knowledge about habitat and socio-economic knowledge about sustaining populations in flood-prone areas. Seven planning units were developed across the Lower Eastern Shore. Single-attribute value functions determined the overall value of each unit per attribute, whereas multi-attribute value functions determined the overall value of each unit for all fundamental objectives. Sensitivity analysis incorporated data adjustments based on different flood scenarios and unit sizes, and variation in attribute weights associated with the multi-attribute value function. The Pareto efficiency principle revealed tradeoffs between units for habitat protection and management and community engagement and adaptation. Results indicate that four units are Pareto efficient under different sensitivity iterations and they trade off value in the fundamental objectives, whereas one unit provides the highest combined habitat and community value. This research guided thinking about equity in decision making and targeting conservation actions at local scales. The approach and methods can be used to inform conservation decisions in other similar contexts.

Identification of the Real Value of Digital Assets for Accounting Purposes

The study involves identifying approaches to estimating the real value of digital assets received by an enterprise, controlled by it, and accounted for at a particular cost. The main purpose of the study is to identify the parameters of estimating the value of the received asset from the type of operations (mining, barter). The study uses the method of description, which allowed to establish certain features of the accounting reflection of cryptocurrency in accounting depending on the type of transaction, recognition, and valuation of such an asset. The study analysed the main operations involving cryptocurrency. It is established that upon transactions with virtual currencies, cryptocurrency, digital assets, special attention should be paid to measuring the consumer value of certain goods (assets). Different approaches to the application of measuring the consumer value of assets are analysed. The possibilities of applying the main types of value are considered, it is established that none of them exhaustively covers the essence of measuring the consumer value of digital assets, virtual currency, and cryptocurrency. The introduction of a new type of value as attributive is proposed and substantiated. The main options for cryptocurrency valuation depending on transactions with such assets are disclosed and options for using a certain type of valuation are recommended. The study provides the calculation of the attributive value depending on certain types of operations. It is established that the proposed attributive value reflects a direct dependence on income and sales of the asset, the cost of such an asset, and its recognition for tax purposes. The study provides a real example of calculating and applying the attribute value of a Bitcoin Cash coin for reporting during the revaluation of an asset

Mining communities and their descriptions on attributed graphs: a survey

Finding communities that are not only relatively densely connected in a graph but that also show similar characteristics based on attribute information has drawn strong attention in the last years. There exists already a remarkable body of work that attempts to find communities in vertex-attributed graphs that are relatively homogeneous with respect to attribute values. Yet, it is scattered through different research fields and most of those publications fail to make the connection. In this paper, we identify important characteristics of the different approaches and place them into three broad categories: those that select descriptive attributes, related to clustering approaches, those that enumerate attribute-value combinations, related to pattern mining techniques, and those that identify conditional attribute weights, allowing for post-processing. We point out that the large majority of these techniques treat the same problem in terms of attribute representation, and are therefore interchangeable to a certain degree. In addition, different authors have found very similar algorithmic solutions to their respective problem.