Signaling Cost Research Articles

A New Lightweight Routing Protocol for Internet of Mobile Things Based on Low Power and Lossy Network Using a Fuzzy-Logic Method

The IoT devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, packet loss, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), Euclidean distance, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost.

Bodies speak louder than words

This article addresses the question: how do Norwegian Jewish parents reflect on brit milah (circumcision) in a context where this practice is frequently criticised? The data are derived from twenty-five in-depth interviews. Drawing on the perspectives of Catherine Bell and Joseph Bulbulia, the text explores circumcision as part of social life. The parental narratives uncover ongoing negotiations occurring between parents and the minority/majority population. The article argues that circumcision serves as a rite of passage as much for parents as for the infant. For most parents, circumcision is utilised to strengthen a Jewish identity and as a cultural strategy to recreate a Jewish cosmos, even if it entails a costly signal, owing to ambivalence and/or majority opposition. Some individuals find the costs too high and discontinue the practice. However, they all desire to manifest Jewish identity, whether through bodily adaptation or the lack thereof. In this manner, bodies speak louder than words.

National or third-party manufacturer? Sourcing strategy of a dominant platform: Signaling game's perspective

The dominant online platforms (e.g., Amazon and JD) in practice have adopted a strategy of introducing their own store brands as a means to engage in competition with national brands inside consumer markets. However, whether to source store brands from national or third-party manufacturers is still a dilemma for platforms. This paper investigates the online platform's optimal sourcing decision for the store brand when the platform possesses private information about the degree of consumer acceptance of the store brand. This paper develops a signaling game in which the better-informed dominant-platform may reveal its private information through profit margins and highlight the signaling effect of price on the sourcing strategy. Due to the signaling cost, we find that asymmetric information has the potential to inhibit the platform from cooperating with the national manufacturer to produce the store brand. Furthermore, the existence of information asymmetry reduces the profit of the platform with superior information, while simultaneously boosting the profit of the less-informed national manufacturer. Interestingly, our findings reveal that under an asymmetric information setting, a higher level of information asymmetry might negatively impact the platform, which contrasts with the situation when information is symmetric. Consequently, the platform gains more advantages by cooperating with a third-party manufacturer with a moderate level of information asymmetry.

Effects of cost and praise‐seeking need on motive inference for those who engage in helping behaviours

AbstractThis study revealed determinants that influence motive inference toward helpers who engage in lifesaving. The costly signalling theory contends that higher costs associated with helping behaviour will promote one's inference of altruistic motives toward a helper. We examined how costs affect third parties' motive inference when controlling for benefits to recipients. In addition, in terms of projection theory, we predicted that the stronger the third parties' praise‐seeking need, the more likely it is they will infer self‐presentational motives toward a helper. We also examined if the praise‐seeking need would moderate the effect of cost on motive inference. Two vignette studies using hypothetical scenarios were conducted. In Study 1 (N = 140) and Study 2 (N = 100), the main effect of costs was found to be significant, indicating that the larger the cost, the more likely it is that a third party inferred altruistic motives toward a helper. Moreover, the main effect (but not the moderation effect) of the praise‐seeking need was also significant, as predicted above. This study provides stronger support for the costly signalling theory and reveals an individual‐difference factor that determines motive inference toward a helper.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

The dynamic charging behavior of electric vehicles (EVs) is causing frequent line-overloading problems and serious power security issues. Controlled and smart charging mechanisms for EVs incorporating demand response (DR) may provide significant operational flexibility to the grid operators and reduce charging costs for EV users. However, controlling EV charging may cause inconvenience to individual EV users. A market-based mechanism is proposed in this research work that allows EV users and households to participate in the network congestion alleviation DR program online while maintaining the power quality. Consumers submit their charging requirements (e.g., charging power and charging deadlines) and load curtailment tolerances to the aggregator. The aggregator is an entity assumed to have a long-term contract with the distribution system operator (DSO) and regularly receives network congestion information on behalf of retail energy users. Lyapunov optimization (LO) framework is used to reschedule the EVs, and household loads using DC optimal power flow (DCOPF) to get the dynamic congestion cost signal (CCS). The developed strategy is tested on a modified IEEE 33-bus radial active distribution network (ADN). Simulation results illustrate that the designed algorithm is promising in mitigating network congestion by ensuring significantly fewer violations of network constraints (i.e., line limits) both in terms of capacity and frequency. It also results in less energy cost as compared to other benchmark algorithms, e.g., greedy algorithm, and provides a guarantee of meeting EV charging, and flexible household loads’ (FHL) delay tolerance constraints. The average curtailment ratio of FHL and the service delay for EV-charging requests are converged to the user-defined limits, i.e., 0.25 for curtailment ratio tolerance and 10 times-lots for EV service delay. Unlike other counterparts, the developed algorithm is especially suitable for real-time applications as the per-slot average computational cost is negligible, i.e., 0.55 s.

Climatic controls and costly signaling: An integrated analysis of Holocene hunting in the Bonneville and Wyoming Basins, USA

In this paper, we explore and integrate different currencies that may underlie large-game hunting to guide a trans-Holocene analysis of variation in artiodactyl utilization using massive archaeofaunal data-sets from predominantly open-air sites from the Bonneville and Wyoming basins. The available empirical data continue to suggest that artiodactyls yield consistently higher return rates than lagomorphs allowing us to leverage predictions from both the prey choice and energetic risk-gain models that the relative importance of artiodactyl hunting should scale closely with climate-based change in their abundance on the landscape. We document with modeled climate data that seasonal variables correlated with the relative frequency of artiodactyl hunting, but that summer temperature had a significant overriding effect in both regions. Controlling for the negative relationship between summer temperature and artiodactyl abundances, we then document enhanced artiodactyl hunting in general and bison more specifically during the Fremont period that is consistent with a costly signaling hypothesis and the unique socio-ecological conditions of this context. Thus, climatic variation and its influence on artiodactyl abundances drives the overall trajectory of Holocene large game hunting variation but measurable and more subtle influences of costly signaling are also detected.

Politeness matters: The role of polite languages in online peer-to-peer lending

Given the limited information for assessing borrowers in online peer-to-peer (P2P) lending, the textual content of loan requests has been recognized as a crucial factor influencing lenders' lending decisions. However, whether politeness, an important but largely neglected linguistic element in loan requests, is related to lenders' decisions remains underexplored. Drawing from signaling theory, this paper seeks to answer two important questions: (1) Is the use of polite language in loan requests related to lenders' decisions? (2) If so, for which types of borrowers or loans is politeness more beneficial? We test our theoretical framework using a sample of 127,044 loan requests from Renrendai, a leading online P2P lending platform in China. Our findings reveal a significant and positive relation between the use of polite language and the likelihood of successful funding. This positive relation is stronger for borrowers with higher credit ratings, higher education levels, as well as for loan requests with lower information richness and higher certainty in language. Our findings suggest that politeness functions as a costless signal that complements costly signals, reducing information asymmetry in P2P lending. This paper bridges the gap between the literature on the role of politeness in individual decision making and information asymmetry in financial markets, providing new insights into the role of language use in P2P lending.

Human settlement density, not late Holocene climate change, influenced Artiodactyla species abundance in northeastern California faunal assemblages

Variation in late Holocene artiodactyl (e.g., deer, pronghorn, bighorn sheep) hunting in northern California has often been examined from a behavioral ecological perspective to understand past foraging and land-use practices and related changes in human behavior, including settlement patterns, technological change, social signaling, large-scale resource intensification, and anthropogenic resource depression. However, rarely are past climate records and proxies of human population density coupled with zooarchaeological estimates of artiodactyl abundance to evaluate the ecological and anthropogenic drivers of changes in species representation over time. To better understand changes in human behavior related to past large game hunting, we disentangle the causal relationships between late Holocene climate change, human demography, and artiodactyl abundance to assess whether climate change, human hunting, or both impacted the abundance of artiodactyls in zooarchaeological assemblages from northeastern California. We evaluate how climatic variability and inferred human settlement density impacted artiodactyl abundance using modeled paleoclimatic reconstructions, distributions of radiocarbon dates, and faunal data derived from 35 archaeological sites and 37,319 individual faunal specimens. Using piecewise structural equation models, we assess the direct and mediating effects between climate, human settlement density, and artiodactyl abundance and determine the individual effects of these variables on large game species representation in zooarchaeological assemblages. We find that expanding human settlement density and related increases in logistical foraging—not late Holocene climate change—directly impacts the relative abundance of artiodactyls in these assemblages. Moreover, our findings reveal that the effects of climate change are entirely mediated through human settlement density. While our study is unable to decipher if observed changes in human behavior were motivated by goals related to energetic maximization or costly signaling, our study does provide a useful framework for separating the effects of human- and climate-induced explanations for variation in species composition in faunal assemblages.

Unlocking the Power of Reporting: Exploring the Link between Voluntary Sustainability Reporting, Customer Behavior, and Firm Value

Voluntary sustainability reporting is becoming more common, as evidenced by the hundreds of organizations that have adopted the Global Reporting Initiative (GRI) standards or similar reporting frameworks within the last decade. This research aims to explore the influence of voluntary sustainability reporting on customer behavior and firm value. Drawing on signaling theory, this paper developed and empirically tested four hypotheses to understand the relationships between the study variables. We collected actual data from the petrochemical companies in Saudi Arabia from 2012 to 2022. Data were analyzed using a fixed-effect panel model. The findings revealed that, in general, sustainability reporting has a negative impact on firm value and customer behavior. Nonetheless, the association between sustainability reporting, firm value, and customer behavior became positive over time. We conclude that sustainability reporting is a costly signal at first but that it ultimately increases firm value as businesses improve their ability to inform stakeholders about sustainability activities and as investors become more adept at assessing report quality. This paper offers several theoretical and managerial implications.

Multipoint communication using a fog-robotic coordinated nodal conveying system for wireless networks

Fog computing is a framework that makes use of the features of both cloud and edge devices to provide quality services, support mobility, reduce delay & latency, and to provide other services to the recent technologies. The fog computing is incorporated in wireless networks using coordinated multi-point communication, which helps to in sending raw bits over a physical link that connects network swarm nodes rather than logical data packets. This, inturn improves system performance and service quality. In the physical layer, the multipoint connection between swarm nodes established during communication in cellular systems consistently reveals limitations in signaling cost, energy consumption, delay and latency. These limitations nevertheless define a wireless network that integrates wide-ranging applications, from independent systems to extended reality (XR) to support minimal network infrastructure. In this paper, the proposed Fog-Robotic Coordinated Nodal Conveying System (FCNCS) helps optimize the data transmission and lowers the signaling cost of coordinated multipoint communication in the physical layer of a wireless system. Besides, optimized link utilization helps to lower signaling costs by 14.3% with improved coordinated multi-point communication network performance efficiency by 85.5% in the physical layer of the wireless framework compared to the existing methods.

Heterogeneous awareness in financial markets

The overlook of some economic scenarios may result in unforeseen negative outcomes for investors. In this paper, we consider an order-driven financial market in which a fraction of the traders is only partially aware of the possible payoffs of a risky asset, but is aware of the possibility of facing unknown contingencies. Investors decide whether to acquire a costly signal about the payoff of the risky asset and whether to buy such asset given their awareness level and their perceived relations among signals, order flows, and prices. We show that as unawareness becomes more severe, the value of the signal to the partially aware traders diminishes. In turn, through its impact on the price, the reduced number of partially aware informed investors increases the incentives of the fully aware to acquire the signal. In the aggregate, the latter effect does not outweigh the former, so that the overall proportion of informed investors in the market is (weakly) decreasing in the unawareness level. As for the equilibrium price, a lower amount of informed traders makes it more difficult for market makers to distinguish between good and bad signals, and this brings the conditional expectations of the price closer to the unconditional one and reduces the price variance.

Ambitious people are more prone to support resource-intensive aid programs

AbstractIn line with Significance Quest Theory (SQT, Kruglanski et al., 2022) and Costly Signaling Theory (CST, Zahavi, 1995), the present research aims to investigate the relationship between individual differences in ambition and support for costly (in terms of investment of personal resources) aid programs. Consistent with SQT, which holds that the quest for significance is a universal need that may lead to any type (e.g., violent or prosocial) of extreme behavior in order to satisfy it, we hypothesized that ambitious (vs. less ambitious) people are more motivated to engage in resource-intensive aid programs. In four studies (Total N = 744), both correlational (Studies 1 and 4) and experimental (Studies 2 and 3), we found a significant positive relationship between levels of ambition and support for resource-intensive aid programs; this relationship was mediated by difficulty perceived as important, i.e., the attribution of high value to difficult tasks and goals (Study 4).

Search and Matching in Political Corruption

Abstract We develop a search and matching model to analyze the dynamics of the political corruption market. This model serves as a framework for evaluating the effectiveness of a set of anti-corruption policies. Contrary to expectations, conventional policies such as enhancing penalties or allocating greater resources to criminal investigations do not universally emerge as the most effective tools. For mitigating small-scale political corruption, the optimal strategy is to curtail corruption signaling, achieved, for instance, through enhancing transparency and competitiveness in the exchanges between entrepreneurs and politicians. For large-scale corruption, raising the costs of corruption signaling proves less effective as a deterrent compared to ex-post policy measures, such as improved detection effectiveness and harsher sanctions.

Decentralised information transmission in the shadow of conflict

Miscalculations due to lack of information are often seen as one of the main causes of war. Yet, a privately informed country has multiple channels to share information and avoid a costly conflict. I study three ways information can be transmitted – sunk cost signals, audience costs, and military build-up. In a fully decentralised setting, where the uninformed country can perfectly adjust its response to the information it learns, the three channels produce very different outcomes. Sunk cost signals never transmit any information. Information transmission is possible with audience costs when the uninformed country sufficiently values peace. With military build-up, information transmission occurs by accident. It is a by-product of the privately informed country’s attempt to increase its strength. I contrast these findings with the case of a constrained uninformed country that can only choose between a limited number of offers.

PAPR Reduction using Huffrith Algorithm with APSK Modulation Technique in 6G System

Non-Orthogonal Multiple Access (NOMA) is very useful in 6G network system requirements, also a solution to improve spectral efficiency and user fairness. NOMA gives a new dimension that increases spectral efficiency, massive connectivity, low transmission latency, and cost of signalling. Hence, NOMA as multiple access schemes can solve user problems from scarce time or frequency domain resources. 6G data traffic reduction by storing the most popular contents in advance at the network edge whereas content caching restricts duplicate of multiple data transmission. The most concerning problem would be the impact of a few subcarriers on the PAPR reduction in NOMA systems is needed to avoid signal degradation. The objectives of this research are to reduce the PAPR. The method applied is by using the Huffrith algorithm with the APSK modulation technique in the NOMA system. The results were obtained by comparing the contribution method with variations of the coding technique, which is the Huffman, Arithmetic, and Huffrith algorithms. Huffrith algorithm is the higher improvement using 512 subcarriers with 8.6 % and PAPR values of 8.5 dB. For 1024 subcarriers get 8.7 dB and 12.12% percentage of improvement.

Hybrid wavelet/Elman NN model for short term cost prediction utilizing developed deer hunting optimizer

The cost signal of electricity in the competitive electrical energy marketplaces is of special importance for all planning and operation activities. Also, the price of electricity has an uncertain nature and various factors affect it in the short and long term. Factors active in the electricity market need to accurately and effectively forecast the electricity price signal to manage risk in the market. For estimating future electricity prices, this research suggests a combined procedure on the basis of Elman neural network model and the wavelet transform. The proposed Elman neural network/wavelet transform forecasted the next hour's power price based on the past 24 h' pricing. This research uses an optimized Elman neural network using a developed deer hunting optimizer and the total model is named Elman neural network/developed deer hunting optimization-wavelet transform. In this paper, Data of Zone Preliminary Billing is used for establishing the training of model and forecasting of performance. The method is then compared with some other published works and the outcomes demonstrate the offered approach superiority toward those for the electrical cost predicting.

Breaking the mold with effective communication: how social initiatives enhance a brand's social perception and catalyze pro-environmental and purchase intentions

PurposeThe purpose of this study is to fill the research gap by investigating the impact of brand social initiatives on both the warmth and competence dimensions of the brand, as well as examining the brand's potential to induce pro-environmental behavioral change among consumers.Design/methodology/approachThe study employed a scenario-based survey design and surveyed 415 consumers in India. The data were analyzed using structural equation modeling (SEM) with SmartPLS 4.0.FindingsThe results show that social initiatives enhance both dimensions of social perception and increase cooperative intentions. Brand competence is more crucial than warmth in predicting behavioral intentions. Brand-cause fit, CSR commitment and initiatives' impact all influence the brand image. A brand's commitment to the cause is more significant than brand-cause fit in determining how consumers evaluate a brand.Practical implicationsThis research provides insights into how social initiatives can enhance brand image and encourage helping intentions through effective communication traits. It can aid brands in building consumer trust, benefiting society and boosting finances. The study offers a framework for effective cause communication strategies, highlighting the importance of commitment to the cause over fitting with the brand's image to elicit desired responses.Originality/valueThis study is a novel attempt that uses the stereotype content model and costly signaling theory to explore how social initiatives influence a brand's warmth and competence and impact consumer behavior. It provides critical insights into the psychological mechanisms behind social initiatives' impact on consumer behavioral intentions.

Weeding out or picking winners in open innovation? Factors driving multi-stage crowd selection on LEGO ideas

Using unique data from the LEGO Ideas platform and a novel approach of algorithm-based abduction, we combine multiple methods to provide new insights into crowd selection. Through qualitative content-coding, interviews, and prior literature, we derive an initial set of variables. We then use machine learning for feature selection and to identify the most important factors for crowd selection. The findings are used to build theory on crowd selection, which we test on a hold-out sample. Our key finding is that ideator and idea characteristics suggested by prior research can predict crowd selection only at early stages. More specifically, crowds rely on ideator status, prior success, and a carefully crafted idea presentation with many images to weed out bad ideas in early stages. However, these characteristics have little bearing in predicting winners at later stages. We explain this with signaling theory, where these ideator and idea characteristics represent strong, costly signals for idea quality and value only in early stages but fade as new signals (such as social signals of popularity and gaining fact traction) emerge. Our study provides two main contributions to research on crowd selection and idea evaluation. First, our approach enables us to prune explanations for crowd selection and guide attention to the factors which matter most. Second, we extend prior work by considering multi-stage crowd selection and highlighting its dynamic nature.

Defaulting Differently: The Political Economy of Sovereign Debt Restructuring Negotiations

Abstract Negotiations to restructure sovereign debt are protracted affairs, and their outcomes, known as “haircuts,” range from 0 to 80 percent creditor losses. Haircuts impact states’ ability to borrow, cost of borrowing, and economic recovery; they also redistribute income—between states and creditors and between domestic interest groups. I conceptualize the interaction between governments and private creditors as a bargaining game where the government’s will to repay is private information. Creditors can make inferences about repayment based on the government’s political economy, but distributional signals are muddled when there are multiple veto players. Where additional uncertainty persists, governments can issue a public declaration of default, triggering costs in international financial markets. This costly signal separates governments that are willing to repay from those that are not and extorts greater concessions as a result. Using data on haircuts and public default declarations in market-based restructurings from 1980 to 2009, I find that governments are more likely to engage in costly signaling when they face heightened domestic constraints. When governments issue public declarations, they are subsequently rewarded with higher haircuts. Defaults do not all look the same, and the economic consequences are varied.

What does prey harvest composition signal to a social audience?: Experimental studies with Aché hunter-gatherers of Paraguay

In small-scale societies hunting is a high-risk, high-reward activity which impacts status and reproductive success. The question of whether men hunt to provision families or as a costly signal of their phenotypic qualities has been hotly debated in the anthropological literature. To shed new light on this question, we explored audience assessments of a hunter's phenotypic quality and desirability as a function of the composition of prey acquired by the hunter. A combination of ranking and forced-choice tasks were administered to 52 informants (46% female, aged 15–76 years) from the Aché hunter-gatherer tribe of Paraguay between May and July of 2015. Ratings of a hunter's provisioning ability, strength, fighting ability, disease resistance, and desirability as a mate or ally were all positively associated with killing large and hard-to-kill prey, and negatively associated with killing hard-to-find prey. However, killing a single large animal resulted in a worse assessment of hunter phenotype and desirability than killing an equivalent biomass of small animals. These findings highlight the potential of small prey hunting as a mechanism for advertising both quality and consistent provisioning ability. Critically, no conflict was observed between the goal of advertising quality/desirability and the goal of effective provisioning, since hunters who acquired more meat, even if the source of the meat was small game, were generally perceived as having better phenotypes and as more desirable.