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Supply Chain
Published in Jianbin Gao, Qi Xia, Kwame Omono Asamoah, Bonsu Adjei-Arthur, Smart Cities, 2023
Jianbin Gao, Qi Xia, Kwame Omono Asamoah, Bonsu Adjei-Arthur
Supposing the Prisoners' Dilemma is repeated in a two-stage game, we use backward induction in the game just like any dynamic game. Because there will be no more strategies to play at the last stage of the game, the game's outcome will be determined at that point. Backward induction is used to solve this, just like any other dynamic game. Because the optimum answer is for Player 1 (P1) to play D regardless of Player 2 (P2's) approach, (D, D) becomes the Nash equilibrium at the final stage of the game, as shown in Table 5.1, and Player 1 obtains a reward of 1. The sub-game is not the initial stage in and of itself. Starting with the first game, the sub-games can be regarded the game.
An integrated game-theoretic and reinforcement learning modeling for multi-stage construction and infrastructure bidding
Published in Construction Management and Economics, 2023
Muaz O. Ahmed, Islam H. El-adaway
Despite the plethora of valuable previous research efforts that have tackled construction and infrastructure bidding processes through developing various bidding models, there is still a lack of research that considers the subcontractors’ exposure to the winner’s curse in MSG. Concerning that, Ahmed et al. (2016) examined the extent of the winner’s curse in construction bidding and compared single-stage bidding with MSG from that perspective. However, the presented model in Ahmed et al. (2016) for MSG considered general contractors’ and subcontractors’ bids as independent from each other and overlooked the impact of the general contractor’s bids on the subcontractors’ exposure to the winner’s curse and their future bid decisions (i.e. backward induction). Backward induction is a game theory terminology refers to the process of reasoning backwards from the end of situation to determine optimal actions (Hedges 2018). As such, Ahmed et al. (2016) emphasized the need for a bidding model that enables mitigation/avoidance of the winner’s curse in MSG while considering the general contractors’ and subcontractors’ bids as interrelated and reflect the impact of general contractor’s bids on the subcontractors’ exposure to the winner’s curse and their future bid decisions.
Decisions and coordination of green e-commerce supply chain considering green manufacturer's fairness concerns
Published in International Journal of Production Research, 2020
Yuyan Wang, Runjie Fan, Liang Shen, Mingzhou Jin
In GECSC, manufacturers are subordinate companies without decision priority. Moreover, manufacturers’ green technology investment will increase their costs, which will put more pressure on their operation. Therefore, manufacturers will be dissatisfied with their investment and position in GECSC, then generate a strong willingness for fairness concern (Wang et al. 2018). In particular, e-platforms hold online shopping carnival (such as Taobao's double eleven online shopping event and JD's 618 promotion) and force manufacturers to cut prices or give gifts, which makes manufacturers attend to concentrate on the income gap between e-platforms and themselves. When the manufacturer has fairness concerns, it will use as a reference target for profit distribution. Drawing on Wang, Yu, and Shen (2019), the manufacturer's utility function can be characterised as where denotes the fairness concerns’ coefficient, and the smaller the , the weaker manufacturer's fairness concerns. Manufacturer makes decisions to maximise his utility. Backward induction is applied to find the optimal decisions. The solution is the same as in subsection 4.2.
Coordinating inventory sharing with retailer's return in the consignment contracts
Published in International Journal of Production Research, 2022
Ping Zhang, King-Wah Pang, Hong Yan
When the inventory sharing option is not considered, the decision sequence of the events is as follows: the dealer decides consignment price w and the return price r charged to the retailer according to the sold quantity and unsold quantity, respectively. Then the retailer decides the order quantity for the whole consignment cycle. We aim at obtaining the Nash equilibrium by backward induction.