[{"main_file_link":[{"url":"https://www.researchgate.net/publication/382338551_Overcoming_Lemon_Markets_with_Business_Reputation_Ecosystem_-A_Multi-agent_Simulation_on_Monetary_Ratings_Research_Paper","open_access":"1"}],"conference":{"location":"Würzburg"},"title":"Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent Simulation on Monetary  Ratings","date_created":"2024-10-16T07:36:56Z","author":[{"full_name":"Ibrahimli, Ulvi","last_name":"Ibrahimli","first_name":"Ulvi"},{"last_name":"Hemmrich","full_name":"Hemmrich, Simon","first_name":"Simon"},{"first_name":"Simon","full_name":"Zauke, Simon","last_name":"Zauke"},{"first_name":"Axel","last_name":"Winkelmann","full_name":"Winkelmann, Axel"}],"oa":"1","date_updated":"2026-04-02T04:31:53Z","citation":{"ieee":"U. Ibrahimli, S. Hemmrich, S. Zauke, and A. Winkelmann, “Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent Simulation on Monetary  Ratings,” Würzburg, 2024.","chicago":"Ibrahimli, Ulvi, Simon Hemmrich, Simon Zauke, and Axel Winkelmann. “Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-Agent Simulation on Monetary  Ratings.” In <i>19. Internationale Tagung Wirtschaftsinformatik (WI24)</i>, 2024.","ama":"Ibrahimli U, Hemmrich S, Zauke S, Winkelmann A. Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent Simulation on Monetary  Ratings. In: <i>19. Internationale Tagung Wirtschaftsinformatik (WI24)</i>. ; 2024.","apa":"Ibrahimli, U., Hemmrich, S., Zauke, S., &#38; Winkelmann, A. (2024). Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent Simulation on Monetary  Ratings. <i>19. Internationale Tagung Wirtschaftsinformatik (WI24)</i>.","bibtex":"@inproceedings{Ibrahimli_Hemmrich_Zauke_Winkelmann_2024, title={Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent Simulation on Monetary  Ratings}, booktitle={19. Internationale Tagung Wirtschaftsinformatik (WI24)}, author={Ibrahimli, Ulvi and Hemmrich, Simon and Zauke, Simon and Winkelmann, Axel}, year={2024} }","short":"U. Ibrahimli, S. Hemmrich, S. Zauke, A. Winkelmann, in: 19. Internationale Tagung Wirtschaftsinformatik (WI24), 2024.","mla":"Ibrahimli, Ulvi, et al. “Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-Agent Simulation on Monetary  Ratings.” <i>19. Internationale Tagung Wirtschaftsinformatik (WI24)</i>, 2024."},"jel":["C30","A12","D4","D82","L14"],"year":"2024","publication_status":"published","quality_controlled":"1","language":[{"iso":"eng"}],"keyword":["Reputation System","Payment as Rating","Multi-Agent Simulation","Lemon Markets"],"user_id":"83557","_id":"56636","status":"public","abstract":[{"text":"Abstract. Business reputation ecosystems are a widely untapped research field. In these ecosystems, agents can selectively exchange (monetary) ratings to in-form about the experienced quality in a market. We build a model for conducting a multi-agent simulation that can be used to simulate and evaluate business rep-utation ecosystems as a new system class. We explore the factual occurring vol-untary payment to create positive (pay) or negative ratings (no pay), selling rat-ings selectively to alleviate information asymmetry, and the workings of counter-ratings to prevent buyers' dishonest ratings. Thereby, we analyze, among others, agent profitability, the occurrence of dishonest ratings, and reputation bias and sensitivity. The results provide simulation-based empirical evidence that the con-cept of monetary reputation systems provides necessary incentives for participa-tion, and high-quality sellers and honest buyers benefit from such a system. The results indicate that counter-ratings prompt buyers","lang":"eng"}],"type":"conference","publication":"19. Internationale Tagung Wirtschaftsinformatik (WI24)"},{"title":"DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning","author":[{"last_name":"Schneider","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"full_name":"Karl, Holger","id":"126","last_name":"Karl","first_name":"Holger"},{"first_name":"Ramin","last_name":"Khalili","full_name":"Khalili, Ramin"},{"first_name":"Artur","full_name":"Hecker, Artur","last_name":"Hecker"}],"date_created":"2022-10-20T16:44:19Z","oa":"1","date_updated":"2022-11-18T09:59:27Z","citation":{"apa":"Schneider, S. B., Karl, H., Khalili, R., &#38; Hecker, A. (2021). <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>.","short":"S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning, 2021.","mla":"Schneider, Stefan Balthasar, et al. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>. 2021.","bibtex":"@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021} }","chicago":"Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>, 2021.","ieee":"S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>. 2021.","ama":"Schneider SB, Karl H, Khalili R, Hecker A. <i>DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>.; 2021."},"year":"2021","has_accepted_license":"1","language":[{"iso":"eng"}],"file_date_updated":"2022-10-20T16:41:10Z","ddc":["004"],"keyword":["mobility management","coordinated multipoint","CoMP","cell selection","resource management","reinforcement learning","multi agent","MARL","self-learning","self-adaptation","QoE"],"user_id":"477","department":[{"_id":"75"}],"project":[{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - C4: SFB 901 - Subproject C4"},{"name":"SFB 901: SFB 901","_id":"1"}],"_id":"33854","file":[{"content_type":"application/pdf","relation":"main_file","date_created":"2022-10-20T16:41:10Z","creator":"stschn","date_updated":"2022-10-20T16:41:10Z","file_name":"preprint.pdf","access_level":"open_access","file_id":"33855","file_size":2521656}],"status":"public","abstract":[{"lang":"eng","text":"Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches."}],"type":"working_paper"}]