Business reputation ecosystems are a widely untapped research field. In these ecosystems, agents can selectively exchange (monetary) ratings to inform 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 reputation ecosystems as a new system class. We explore the factual occurring voluntary payment to create positive (pay) or negative ratings (no pay), selling ratings 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 concept of monetary reputation systems provides necessary incentives for participation, and high-quality sellers and honest buyers benefit from such a system. The results indicate that counter-ratings prompt buyers to rate sellers honestly, albeit incurring the monetary cost for positive ratings.

This paper analyses the impact of a map-matching algorithm on the
uncertainties of GPS coordinates in the calculation of carbon dioxide emissions
for trailer transport based on telemetry data. Using trailer type, load, journey and
route parameters, the Transport Carbon Footprint can be calculated based on DIN
EN ISO 14083 and using the GLEC Framework. A Monte Carlo simulation was
used to analyse the effects of GPS coordinate uncertainties on the Graphhopper
map matching algorithm. Based on this, the effects of route deviations on the
calculation of the Transport Carbon Footprint were analysed. The results of this
investigation indicate that the use of a map matching algorithm leads to a relative
deviation of less than 0.02 % in the calculation of the carbon footprint of transport
due to GPS uncertainties.