Skip to main content
Log in

Cooperation in dynamic multicriteria games with random horizons

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

In this paper a new approach to construct the cooperative behavior in dynamic multicriteria games is presented. To obtain a multicriteria Nash equilibrium the bargaining construction (Nash product) is adopted. To design a multicriteria cooperative equilibrium Nash bargaining scheme is applied with the multicriteria Nash equilibrium payoffs playing the role of the status quo points. Dynamic multicriteria bioresource management problem with random harvesting times is considered. The players’ strategies and the payoffs are obtained under cooperative and noncooperative behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Patrone, F., Pusillo, L., Tijs, S.H.: Multicriteria games and potentials. Top 15, 138–145 (2007)

    Article  MathSciNet  Google Scholar 

  2. Pusillo, L., Tijs, S.: E-equilibria for multicriteria games. Ann. ISDG 12, 217–228 (2013)

    MathSciNet  MATH  Google Scholar 

  3. Rettieva, A.N.: A discrete-time bioresource management problem with asymmetric players. Autom. Remote Control 75(9), 1665–1676 (2014)

    Article  Google Scholar 

  4. Rettieva, A.N.: Equilibria in dynamic multicriteria games. IGTR 19(1), 1750002 (2017)

    MathSciNet  MATH  Google Scholar 

  5. Shapley, L.S.: Equilibrium points in games with vector payoffs. Nav. Res. Logist. Q. 6, 57–61 (1959)

    Article  MathSciNet  Google Scholar 

  6. Voorneveld, M., Grahn, S., Dufwenberg, M.: Ideal equilibria in noncooperative multicriteria games. Math. Methods Oper. Res. 52, 65–77 (2000)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Shandong Province “Double-Hundred Talent Plan” (No. WST2017009).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anna N. Rettieva.

Appendices

A Proof of the relations

We give a proof for player 1’s first criterion (i.e., find a relationship between \(V_1^{1c}(\tau ,x)\) and \(V_1^{1c}(\tau +1,x)\)). In the case of player 2 and both players’ second criteria, the line of reasoning is the same. Using (29), construct the Bellman function of player 1 as the game reaches step \(\tau \):

$$\begin{aligned}&V_1^{1c}(\tau ,x)=\max _{u_{1\tau }^c,\ldots ,u_{1n}^c} \left\{ \frac{\theta _{\tau }}{\sum \nolimits _{l=\tau }^n \theta _l} \sum _{n_2=\tau }^{n} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \delta ^{\tau } p_1 u_{1\tau }^c\right. \\&\left. \qquad +\,\sum _{n_1=\tau +1}^{n} \frac{\theta _{n_1}}{\sum \nolimits _{l=\tau }^n \theta _l} \left[ \sum _{n_2=n_1}^{n} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \sum _{t=\tau }^{n_1}\delta ^t p_1 u_{1t}^c +\sum _{n_2=\tau }^{n_1-1} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \sum _{t=\tau }^{n_2}\delta ^t p_1 u_{1t}^c+V_1^a(\tau ,n_1)\right] \right\} \\&\quad =\frac{\theta _{\tau }}{\sum \nolimits _{l=\tau }^n \theta _l} \delta ^{\tau } p_1 u_{1\tau }^c + \sum _{n_1=\tau +1}^{n} \frac{\theta _{n_1}}{\sum \nolimits _{l=\tau }^n \theta _l} \left[ \sum _{n_2=n_1}^{n} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \left( \sum _{t=\tau +1}^{n_1}\delta ^t p_1 u_{1t}^c+\delta ^{\tau } p_1u_{1\tau }^c\right) \right. \\&\left. \qquad +\sum _{n_2=\tau }^{n_1-1} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \left( \sum _{t=\tau +1}^{n_2}\delta ^t p_1 u_{1t}^c+\delta ^{\tau } p_1 u_{1\tau }^c\right) +V_1^a(\tau ,n_1)\right] =\delta ^{\tau } p_1 u_{1\tau }^c +\sum _{n_1=\tau +1}^{n} \frac{\theta _{n_1}}{\sum \nolimits _{l=\tau }^n \theta _l} \\&\qquad \cdot \left[ \sum _{n_2=n_1}^{n} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \sum _{t=\tau +1}^{n_1}\delta ^t p_1 u_{1t}^c +\sum _{n_2=\tau +1}^{n_1-1} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau }^n \omega _l} \sum _{t=\tau +1}^{n_2}\delta ^t p_1 u_{1t}^c+V_1^a(\tau ,n_1)\right] \\&\quad =\delta ^{\tau } p_1 u_{1\tau }^c+ \sum _{n_1=\tau +1}^{n} \frac{\theta _{n_1}}{\sum \nolimits _{l=\tau +1}^n \theta _l} \frac{\sum \nolimits _{l=\tau +1}^n\theta _l}{\sum \nolimits _{l=\tau }^n\theta _l} \left[ \sum _{n_2=n_1}^{n} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau +1}^n \omega _l} \frac{\sum \nolimits _{l=\tau +1}^n\omega _l}{\sum \nolimits _{l=\tau }^n\omega _l}\sum _{t=\tau +1}^{n_1}\delta ^t p_1 u_{1t}^c\right. \\&\left. \qquad +\sum _{n_2=\tau +1}^{n_1-1} \frac{\omega _{n_2}}{\sum \nolimits _{l=\tau +1}^n \omega _l} \frac{\sum \nolimits _{l=\tau +1}^n\omega _l}{\sum \nolimits _{l=\tau }^n\omega _l} \sum _{t=\tau +1}^{n_2}\delta ^t p_1 u_{1t}^c + \frac{\omega _{\tau }}{\sum \nolimits _{l=\tau }^{n}\omega _l}\sum _{t=\tau }^{n_1}\delta ^t p_1 u_{1t}^a +\frac{\sum \nolimits _{l=\tau +1}^{n}\omega _l}{\sum \nolimits _{l=\tau }^{n}\omega _l} V_1^a(\tau +1,n_1)\right] \\&\quad =\delta ^{\tau } p_1 u_{1\tau }^c+ P_{\tau }^{\tau +1} V_1^{1c}(\tau +1,x) +C_{1\tau }\sum _{n_1=\tau +1}^n \theta _{n_1}\sum _{t=\tau }^{n_1}\delta ^t p_1 u_{1t}^a, \end{aligned}$$

where

$$\begin{aligned} P_{\tau }^{\tau +1}=\frac{\sum \nolimits _{l=\tau +1}^n\omega _l}{\sum \nolimits _{l=\tau }^n\omega _l} \frac{\sum \nolimits _{l=\tau +1}^n\theta _l}{\sum \nolimits _{l=\tau }^n\theta _l}, C_{1\tau } =\frac{\omega _{\tau }}{\sum \nolimits _{l=\tau }^{n}\omega _l} \frac{1}{\sum \nolimits _{l=\tau }^{n}\theta _l} . \end{aligned}$$

Similarly, we establish a relationship between \(V_2^{1c}(\tau ,x)\) and \(V_2^{1c}(\tau +1,x)\) in the form

$$\begin{aligned} V_2^{1c}(\tau ,x)=\delta ^{\tau } p_2 u_{2\tau }^c + P_{\tau }^{\tau +1} V_2^{1c}(\tau +1,x)+ C_{2\tau }\sum _{n_2=\tau +1}^n\omega _{n_2}\sum _{t=\tau }^{n_2}\delta ^t p_2 u_{2t}^a, \end{aligned}$$

where

$$\begin{aligned} C_{2\tau } =\frac{\theta _{\tau }}{\sum \nolimits _{l=\tau }^{n}\theta _l} \frac{1}{\sum \nolimits _{l=\tau }^{n}\omega _l}. \end{aligned}$$

Hence,

$$\begin{aligned} V_1^{c}(\tau ,x)= & {} V_1^{1c}(\tau ,x)+V_2^{1c}(\tau ,x)=\delta ^{\tau }(p_1 u_{1\tau }^c+p_2 u_{2\tau }^c)+ P_{\tau }^{\tau +1} V_1^{c}(\tau +1,x)\\&+C_{1\tau }\sum _{n_1=\tau +1}^n\theta _{n_1}\sum _{t=\tau }^{n_1}\delta ^t p_1 u_{1t}^a +C_{2\tau }\sum _{n_2=\tau +1}^n\omega _{n_2}\sum _{t=\tau }^{n_2}\delta ^t p_2 u_{2t}^a. \end{aligned}$$

According to the above, by analogy we get the relations for the second cooperative payoffs of both players.

B Proof of Theorem 2

Our analysis begins with the occurrence of step n. Both players have zero payoffs at the next step \(n+1\). Hence, the optimal cooperative strategies coincide with the Nash equilibrium ones, and the payoffs have the form

$$\begin{aligned} V_1^{c}(n,x)=A_1 x, V_2^{c}(n,x)=A_2 x^2 . \end{aligned}$$

Now, suppose that the game reaches step \(n-1\). In this case, the problem (35) reduces to

$$\begin{aligned} (V_1^{c}(n-1,x)-A_1 x)(V_2^{c}(n-1,x)-A_2 x^2) \rightarrow \max _{u_{1n-1}^c,u_{2n-1}^c}, \end{aligned}$$
(40)

where

$$\begin{aligned} V_1^{c}(n-1,x)= & {} \delta ^{n-1} (p_1 u_{1 n-1}^c+p_2 u_{2 n-1}^c)+P_{n-1}^{n} V_1^{c}(n,\varepsilon x-u_{1 n-1}^c-u_{2 n-1}^c)\\&+C_{1 n-1}\theta _n\delta ^n p_1 u_{1n}^a +C_{2 n-1}\omega _n\delta ^n p_2 u_{2n}^a, \\ V_2^{c}(n-1,x)= & {} -2\delta ^{n-1}m u_{1 n-1}^c u_{2 n-1}^c + P_{n-1}^{n} V_2^{c}(n,\varepsilon x-u_{1 n-1}^c-u_{2 n-1}^c). \end{aligned}$$

As usual, we seek for the linear strategies of the form \(u_{i n-1}^c=\gamma _{i n-1}^c x\). From the first-order conditions we get the relation

$$\begin{aligned} \gamma _{2 n-1}^c= & {} \frac{\varepsilon A_2 P_{n-1}^n(p_1-p_2)}{m(\delta ^{n-1}p_1-P_{n-1}^n A_1)+P_{n-1}^n A_2(p_1-p_2)}\nonumber \\&+\gamma _{1 n-1}^c\frac{ m(\delta ^{n-1}p_1-P_{n-1}^n A_1)-P_{n-1}^n A_2(p_1-p_2)}{m(\delta ^{n-1}p_1-P_{n-1}^n A_1)+P_{n-1}^n A_2(p_1-p_2)}. \end{aligned}$$
(41)

Now, we study the situation when step \(n-2\) occurs in the game. Then the problem (35) takes the form

$$\begin{aligned} \left( V_1^{c}(n-2,x)-A_1 x\right) \left( V_2^{c}(n-2,x)-A_2 x^2\right) \rightarrow \max _{u_{1n-2}^c,u_{2n-2}^c}, \end{aligned}$$
(42)

where

$$\begin{aligned} V_1^c(n-2,x)= & {} \delta _1^{n-2} (p_1 u_{1 n-2}^c+p_2 u_{2 n-2}^c) +P_{n-2}^{n-1} V_1^c(n-1,\varepsilon x-u_{1 n-2}^c-u_{2 n-2}^c)\\&+\,C_{1 n-2}\sum \limits _{t=n-1}^n\theta _{t}\delta ^t p_1 u_{1t}^a+ C_{2 n-2}\sum \limits _{t=n-1}^n\omega _{t}\delta ^t p_2 u_{2t}^a, \\ V_2^c(n-2,x)= & {} -2m\delta ^{n-2} u_{1 n-2}^c u_{2 n-2}^c+ P_{n-2}^{n-1} V_2^c(n-1,\varepsilon x-u_{1 n-2}^c-u_{2 n-2}^c). \end{aligned}$$

Searching for the linear strategies \(u_{i n-2}^c=\gamma _{i n-2}^c x\), from the first-order optimality conditions we obtain the following relationship between equilibrium strategies of the players when step \(n-2\) occurs in the game:

$$\begin{aligned}&\gamma _{2 n-2}^c=1/\Bigl (m\Bigl [\delta ^{n-2}p_2-P_{n-2}^{n-1}(\delta ^{n-1}(p_1\gamma ^c_{1n-1} +p_2\gamma ^c_{2n-1})\nonumber \\&\quad +P_{n-2}^{n-1}A_1(\varepsilon -\gamma ^c_{1n-1}-\gamma ^c_{2n-1}))\nonumber \\&\quad +P_{n-2}^{n-1}(p_1-p_2)\Bigl (-2\delta ^{n-1}m\gamma ^c_{1n-1}\gamma ^c_{2n-1}+P_{n-2}^{n-1} A_2 (\varepsilon -\gamma ^c_{1n-1}-\gamma ^c_{2n-1})^2\Bigr )\Bigr ]\Bigr )\nonumber \\&\quad \cdot \varepsilon P_{n-2}^{n-1}(p_1-p_2)(-2\delta ^{n-1}m \gamma ^c_{1n-1}\gamma ^c_{2n-1})+P_{n-2}^{n-1}A_2(\varepsilon -\gamma ^c_{1n-1} -\gamma ^c_{2n-1})^2\nonumber \\&\quad +\gamma ^c_{1n-2}\Bigl [\delta ^{n-2}p_1-P_{n-2}^{n-1}(\delta ^{n-1} (p_1\gamma ^c_{1n-1}+p_2\gamma ^c_{2n-1})\nonumber \\&\quad +P_{n-2}^{n-1}A_1(\varepsilon -\gamma ^c_{1n-1}-\gamma ^c_{2n-1}))\nonumber \\&\quad -P_{n-2}^{n-1}(p_1-p_2)\Bigl (-2\delta ^{n-1}m\gamma ^c_{1n-1}\gamma ^c_{2n-1}+P_{n-2}^{n-1} A_2 (\varepsilon -\gamma ^c_{1n-1}-\gamma ^c_{2n-1})^2\Bigr )\Bigr ] \, \end{aligned}$$
(43)

and the relationship between equilibrium strategies of the players when the game reaches steps \(n-1\) and \(n-2\):

$$\begin{aligned} \frac{\gamma _{2 n-2}^c-\gamma _{1 n-2}^c}{\varepsilon - \gamma _{1 n-2}^c-\gamma _{2 n-2}^c}=\delta \left( \gamma _{2 n-1}^c-\gamma _{1 n-1}^c\right) . \end{aligned}$$
(44)

By continuing the described process until the game reaches step k, we easily obtain the payoffs (36), (37) and the cooperative strategies of the form (38), (39).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rettieva, A.N. Cooperation in dynamic multicriteria games with random horizons. J Glob Optim 76, 455–470 (2020). https://doi.org/10.1007/s10898-018-0658-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-018-0658-6

Keywords

Mathematics Subject Classification

Navigation