Browsing by Author "Alper, S."

Now showing 1 - 2 of 2

Citation - Scopus: 2
Reflection Predicts and Leads To Decreased Conspiracy Belief
(Elsevier B.V., 2025) Bayrak, F.; Sümer, V.; Dogruyol, B.; Saribay, S.A.; Alper, S.; Isler, O.; Yilmaz, O.
Recent research indicates a generally negative relationship between reflection and conspiracy beliefs. However, most of the existing research relies on correlational data on WEIRD (Western, Educated, Industrialized, Rich, Democratic) populations. The few existing experimental studies are limited by weak manipulation techniques that fail to reliably activate cognitive reflection. Hence, questions remain regarding (1) the consistency of the negative relationship between conspiracy beliefs and cognitive reflection, (2) the extent of cross-cultural variation and potential moderating factors, and (3) the presence of a causal link between cognitive reflection and conspiracy beliefs. In two preregistered studies, we investigated the association between cognitive reflection and conspiracy beliefs. First, we studied the correlation between two variables across 48 cultures and investigated whether factors such as WEIRDness and narcissism (personal and collective) moderate this relationship. In the second study, we tested the causal effect of reflection using a reliable and effective manipulation technique—debiasing training—on both generic and specific conspiracy beliefs. The first study confirmed the negative association between reflection and belief in conspiracy theories across cultures, with the association being notably stronger in non-WEIRD societies. Both personal and collective narcissism played significant moderating roles. The second study demonstrated that debiasing training significantly decreases both generic and COVID-19 conspiracy beliefs in a non-WEIRD context, with more pronounced effects for general conspiracy beliefs. Our research supports that reflection is a consistent cross-cultural predictor of conspiracy beliefs and that activating reflection can reduce such beliefs through rigorous experimental interventions. © 2025 Elsevier B.V.
Hosting Capacity Calculation Methods
(Elsevier, 2025) Oguzhan, C.; Alper, S.
In this chapter, we focus on hosting capacity (HC) calculations, by giving the methods to determine the maximum amount of distributed energy resources (DER) that can be integrated into power distribution network(s) without compromising reliability or performance. We detail methodologies such as power flow-based approaches, probabilistic techniques, and machine learning algorithms, with sample applications of HC calculations. Initially, we focus on power flow-based methods based on simulating power distribution network(s) to assess system voltage, current flow, and stability impacts from DER installations. Then, we will give the probabilistic approaches that use uncertainties in renewable generation and consumer demand, based on statistical techniques and Monte Carlo simulations aiming to reflect these variability. Machine learning (ML) techniques will also be given based on analyzing large data sets, detecting patterns, and predicting system responses. These kinds of methods include regression analysis and neural networks trained on historical data for optimized HC predictions. It should be stated that HC is impacted by several factors, such as network topology, load profiles, and DER characteristics, and these as well will be discussed. We will provide a practical example of an HC calculation on a 141-node distribution network using a step-by-step algorithm in Matpower, with simulation results based on an iterative deterministic method. Then, we will give the broader implications of HC assessments for grid modernization and energy policy, highlighting how accurate calculations support a more decentralized, sustainable, and resilient energy future. © 2025 Elsevier Inc. All rights reserved.