I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

Public procurement amounts to a significant part of Sweden's GDP. Nevertheless, it is an overlooked sector characterized by low digitization and inefficient competition where bids are not submitted based on proper mathematical tools. This Thesis seeks to create a structured approach to bidding in cleaning services by determining factors affecting the participation and pricing decision of potential buyers. Furthermore, we assess price prediction by comparing multiple linear regression models (MLR) to support vector regression (SVR). In line with previous research in the construction sector, we find significance for several factors such as project duration, location and type of contract on the participation decision in the cleaning sector. One notable deviant is that we do not find contract size to have an impact on the pricing decision. Surprisingly, the performance of MLR are comparable to more advanced SVR models. Stochastic dominance tests on price performance concludes that experienced bidders perform better than their inexperienced counterparts and companies place more competitive bids in lowest price tenders compared to economically most advantageous tenders (EMAT) indicating that EMAT tenders are regarded as unstructured. However, no significance is found for larger actors performing better in bidding than smaller companies. / Offentliga upphandlingar utgör en signifikant del av Sveriges BNP. Trots detta är det en förbisedd sektor som karakteriseras av låg digitalisering och ineffektiv konkurrens där bud läggs baserat på intuition snarare än matematiska modeller. Denna avhandling ämnar skapa ett strukturerat tillvägagångssätt för budgivning inom städsektorn genom att bestämma faktorer som påverkar deltagande och prissättning. Vidare undersöker vi prisprediktionsmodeller genom att jämföra multipel linjära regressionsmodeller med en maskininlärningsmetod benämnd support vector regression. I enlighet med tidigare forskning i byggindustrin finner vi att flera faktorer som typ av kontrakt, projekttid och kontraktsplats har en statistisk signifikant påverkan på deltagande i kontrakt i städindustrin. En anmärkningsvärd skillnad är att kontraktsvärdet inte påverkar prissättning som tidigare forskning visat i andra områden. För prisprediktionen är det överraskande att den enklare linjära regressionsmodellen presterar jämlikt till den mer avancerade maskininlärningsmodellen. Stokastisk dominanstest visar att erfarna företag har en bättre precision i sin budgivning än mindre erfarna företag. Därtill lägger företag överlag mer konkurrenskraftiga bud i kontrakt där kvalitetsaspekter tas i beaktning utöver priset. Vilket kan indikera att budgivare upplever dessa kontrakt som mindre strukturerade. Däremot finner vi ingen signifikant skillnad mellan större och mindre företag i denna bemärkning.