Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for a given coordination graph is therefore a central subroutine in cooperative multi-agent reinforcement learning (MARL). Much research in MARL focuses on how to gradually update the parameters of the coordination graph, whilst leaving the solving of the coordination graph up to a known typically exact and generic subroutine. However, exact methods { e.g., Variable Elimination { do not scale well, and generic methods do not exploit the MARL setting of gradually updating a coordination graph and recomputing the joint action to select. In this paper, we examine what happens if we use a heuristic method, i.e., local search, to select joint actions in MARL, and whether we can use outcome of this local search from a previous time-step to speed up and improve local search. We show empirically that by using local search, we can scale up to many agents and complex coordination graphs, and that by reusing joint actions from the previous time-step to initialise local search, we can both improve the quality of the joint actions found and the speed with which these joint actions are found.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Social media firestorms pose a significant challenge for firms in the digital age. Tackling firestorms is difficult because the judgments and responses from social media users are influenced by not only the nature of the transgressions but also by the reactions and opinions of other social media users. Drawing on the heuristic-systematic information processing model, we propose a research model to explain the effects of social impact (the heuristic mode) and argument quality and moral intensity (the systematic mode) on perceptions of firm wrongness (the judgment outcome) as well as the effects of perceptions of firm wrongness on vindictive complaining and patronage reduction. We adopted a mixed methods approach in our investigation, including a survey, an experiment, and a focus group study. Our findings show that the heuristic and systematic modes of information processing exert both direct and interaction effects on individuals’ judgment. Specifically, the heuristic mode of information processing dominates overall and also biases the systematic mode. Our study advances the literature by offering an alternative explanation for the emergence of social media firestorms and identifying a novel context in which the heuristic mode dominates in dual information processing. It also sheds light on the formulation of response strategies to mitigate the adverse impacts resulting from social media firestorms. We conclude our paper with limitations and future research directions.
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298 woorden: In the upcoming years the whole concept of mobility will radically change. Decentralization of energy generation, urbanization, digitalization of processes, electrification of vehicles and shared mobility are only some trends which have a strong influence on future mobility. Furthermore, due to the shift towards renewable energy production, the public and the private sector are required to develop new infrastructures, new policies as well as new business models. There are countless opportunities for innovative business models emerging. Companies in this field – such as charging solution provider, project management or consulting companies that are part of this project, Heliox and Over Morgen respectively – are challenged with countless possibilities and increasing complexity. How to overcome this problem? Academic research proposes a promising approach, namely the use of business model patterns for business model innovation. In short, these business model patterns are descriptions of proven practical solutions to common business model challenges. An example for a general pattern would be the business model pattern “Consumables”. It describes how to lock in a customer into an ecosystem by using a subsidized basic product and complement it with overpriced consumables. This pattern works really well and has been used by many companies (e.g. Senseo, HP, or Gillette). To support the business model innovation process of Heliox and Over Morgen as well as companies in the electric mobility space in general, we propose to systematically consolidate and develop business model patterns for the electric mobility sector and to create a database. Electric mobility patterns could not only foster creativity in the business model innovation process but also enhance collaboration in teams. By having a classified list of business model pattern for electric mobility, practitioners are equipped which a heuristic tool to create, extend and revise business models for the future.
Veel mkb ondernemers maken gebruik van een financieel adviseur bij belangrijke financiële beslissingen. Momenteel is er echter weinig inzicht in achterliggende psychosociale factoren die het financiële advies van financieel adviseurs beïnvloeden. Op basis van eerder onderzoek (Kahneman, 2013) blijkt dat mensen zich laten leiden, als het gaat om financiële beslissingen, door een keur aan psychologische ‘denkvalkuilen’ (‘heuristics’ en biases’). Verondersteld wordt dat rol van de adviseur zou moeten zijn om specialistisch en objectief advies te geven, echter de denkvalkuilen en vooroordelen van de cliënt ‘klinken ook door’ in het advies van hun financieel adviseurs. Zo worden irrationele vormen van risicoperceptie en irreële verwachtingen van cliënten ten aanzien van de toekomst meegenomen in de adviezen van adviseurs; “de klant is immers koning.” Eerder onderzoek suggereert dat financieel adviseurs de verwachtingen en ideeën van hun cliënten alleen maar bevestigen en niet, indien nodig, bij irreële of ‘foute’ verwachtingen of aannames, corrigeren. De beweegreden van de adviseur hiervoor zijn dat meegaan met de ideeën van cliënten resulteert in minder verantwoordelijkheid voor de adviseur bij negatieve uitkomsten of resultaten; “dit is immers wat de cliënt zelf wil.” Terwijl veel input en advies het tegenovergestelde bewerkstelligt, “de cliënt vaart blind op de adviezen van zijn adviseur”, wat de cliënt-adviseur relatie in gevaar brengt bij negatieve resultaten. Het gevolg is een suboptimaal en in sommige gevallen slecht financieel advies. Dit onderzoek heeft tot doel de voorwaarden van een opener, beter afgewogen en objectiever financieel advies te ontdekken voor mkb-ondernemers, De centrale vraag is: Hoe komt een financieel adviseur tot een financieel advies voor mkb bedrijven? Deze vraag zal beantwoord worden de een survey bij de twee grootste franchise financiële advieskantoren (+/- 2500 leden), wat een representatieve steekproef is voor de financieel adviseurs in het mkb.
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