---
_id: '56636'
abstract:
- lang: eng
  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
author:
- first_name: Ulvi
  full_name: Ibrahimli, Ulvi
  last_name: Ibrahimli
- first_name: Simon
  full_name: Hemmrich, Simon
  last_name: Hemmrich
- first_name: Simon
  full_name: Zauke, Simon
  last_name: Zauke
- first_name: Axel
  full_name: Winkelmann, Axel
  last_name: Winkelmann
citation:
  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} }'
  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.
  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.
  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.
  short: 'U. Ibrahimli, S. Hemmrich, S. Zauke, A. Winkelmann, in: 19. Internationale
    Tagung Wirtschaftsinformatik (WI24), 2024.'
conference:
  location: Würzburg
date_created: 2024-10-16T07:36:56Z
date_updated: 2026-04-02T04:31:53Z
jel:
- C30
- A12
- D4
- D82
- L14
keyword:
- Reputation System
- Payment as Rating
- Multi-Agent Simulation
- Lemon Markets
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.researchgate.net/publication/382338551_Overcoming_Lemon_Markets_with_Business_Reputation_Ecosystem_-A_Multi-agent_Simulation_on_Monetary_Ratings_Research_Paper
oa: '1'
publication: 19. Internationale Tagung Wirtschaftsinformatik (WI24)
publication_status: published
quality_controlled: '1'
status: public
title: Overcoming Lemon Markets with Business Reputation  Ecosystem – A Multi-agent
  Simulation on Monetary  Ratings
type: conference
user_id: '83557'
year: '2024'
...
---
_id: '33854'
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."
author:
- first_name: Stefan Balthasar
  full_name: Schneider, Stefan Balthasar
  id: '35343'
  last_name: Schneider
  orcid: 0000-0001-8210-4011
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
- first_name: Ramin
  full_name: Khalili, Ramin
  last_name: Khalili
- first_name: Artur
  full_name: Hecker, Artur
  last_name: Hecker
citation:
  ama: 'Schneider SB, Karl H, Khalili R, Hecker A. <i>DeepCoMP: Coordinated Multipoint
    Using Multi-Agent Deep Reinforcement Learning</i>.; 2021.'
  apa: 'Schneider, S. B., Karl, H., Khalili, R., &#38; Hecker, A. (2021). <i>DeepCoMP:
    Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning</i>.'
  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.'
  mla: 'Schneider, Stefan Balthasar, et al. <i>DeepCoMP: Coordinated Multipoint Using
    Multi-Agent Deep Reinforcement Learning</i>. 2021.'
  short: 'S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint
    Using Multi-Agent Deep Reinforcement Learning, 2021.'
date_created: 2022-10-20T16:44:19Z
date_updated: 2022-11-18T09:59:27Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
  content_type: application/pdf
  creator: stschn
  date_created: 2022-10-20T16:41:10Z
  date_updated: 2022-10-20T16:41:10Z
  file_id: '33855'
  file_name: preprint.pdf
  file_size: 2521656
  relation: main_file
file_date_updated: 2022-10-20T16:41:10Z
has_accepted_license: '1'
keyword:
- mobility management
- coordinated multipoint
- CoMP
- cell selection
- resource management
- reinforcement learning
- multi agent
- MARL
- self-learning
- self-adaptation
- QoE
language:
- iso: eng
oa: '1'
project:
- _id: '4'
  name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
  name: 'SFB 901 - C4: SFB 901 - Subproject C4'
- _id: '1'
  name: 'SFB 901: SFB 901'
status: public
title: 'DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning'
type: working_paper
user_id: '477'
year: '2021'
...