## 3 Model Analysis under the Static Reward and Punishment Mechanism
### Analysis of Results  
According to the stability analysis of the strategy combination in 3.4, the local equilibrium points \(E_{1}(0,0,0)\), \(E_{2}(1,0,0)\), \(E_{3}(0,1,0)\), \(E_{4}(0,0,1)\), \(E_{5}(1,1,0)\), \(E_{6}(1,0,1)\), \(E_{7}(0,1,1)\) and \(E_{8}(1,1,1)\) are all saddle points, and certain conditions must be met to become the stable points of the system. Under the assumption of unconstrained constraints, it is clear that there is no equilibrium point in the shared system. In terms of shared manufacturing practice, it is difficult for decision makers to make theoretically optimal behavioral decisions due to bounded rationality and different risk attitudes of decision makers, resulting in systematic deviations in the behavior of shared participants, and it is difficult for the shared system to achieve the optimal state. This paper examines the non-theoretical optimal behavior decision of decision makers and the non-optimal state of shared systems from the perspective of \"cost-benefit\" from the perspective of valence-cost perception bias, reference dependence, risk preference, and so on. A detailed explanation is provided below. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equilibrium Point** & \(\lambda 1,\lambda 2,\lambda 3\) & **Sign** & **Stability** \\ \hline \((0,0,0)\) & \(B,F,N\) & \((\times,\times,\times)\) & saddle point \\ \((1,0,0)\) & \(-B,E,M\) & \((\times,\times,\times)\) & saddle point \\ \((0,1,0)\) & \(A,F,N\) & \((\times,\times,\times)\) & saddle point \\ \((0,0,1)\) & \(B,F,-N\) & \((\times,\times,\times)\) & saddle point \\ \((1,1,0)\) & \(-A,-E,M\) & \((\times,\times,\times)\) & saddle point \\ \((1,0,1)\) & \(-B,E,-M\) & \((\times,\times,\times)\) & saddle point \\ \((0,1,1)\) & \(A,-F,-N\) & \((\times,\times,\times)\) & saddle point \\ \((1,1,1)\) & \(-A,-E,-M\) & \((\times,\times,\times)\) & saddle point \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Strategy portfolio stability analysis. Valence-cost perception bias  
The valence perception of supply side quality improvement, demand side cost sharing, and active shared platform supervision is often lower than that of its opposing behavior in the shared manufacturing quality synergy model; supply side quality improvement, demand side cost sharing, and active shared platform supervision's cost perception is often higher than that of its opposing behavior. The choice of behavioral decision-making is the consequence of balancing the advantages and negatives in terms of valence-perceived bias, but the appearance of the free-rider strategy choice will disrupt this condition. When the cost is shared by the supply side, the supply side can choose not to increase the quality in order to get \"extra income,\" and the demand side can choose not to pay the cost when the supply side's quality is improved in order to reap the advantages. To save money on self-discipline, adopt passive supervision. In terms of cost-perceived variation, the shared platform dynamically matches the supply and demand sides based on big data and sophisticated algorithms in the shared manufacturing mode, and this dynamic matching process has higher uncertainty. Because the creation, distribution, and consumption of shared services are synchronous operations, the shared platform must establish a more flexible and responsive supply chain than the traditional supply chain, and the supply and demand sides must work harder to match resource supply and demand. Reference dependency  
Prospect theory holds that before making a choice, people will select a reference point and then evaluate the difference between the behavioral result and the reference point. The Mental Accounts Theory splits the behavioral reference point further on this basis into a valence reference point and a cost reference point. Because of the existence of the reference effect, the establishment of distinct reference points for different actions will result in varied assessment outcomes for the same conduct, influencing behavior choice.