Industrial Symbiotic Networks (s) are collaborative networks of industries with the aim to reduce their materials and energy footprint by circulating reusable resources (e.g, physical waste material) among the network members Chertow (2000); Lombardi and Laybourn (2012); Yazan et al. (2016). Such a symbiosis leads to socioeconomic and environmental benefits for involved firms and the society. One barrier against stable implementations is the lack of frameworks able to secure such networks against unfair and unstable allocation of obtainable benefits among the involved firms. In other words, even if economic benefits are foreseeable, lack of stability and/or fairness may lead to non-cooperative decisions and hence unimplementability of s ( implementation problem). Reviewing recent contributions in the field of industrial symbiosis research, we encounter studies focusing on the interrelations between industrial enterprises Yazan et al. (2016) and the role of contracts in the process of implementation Albino et al. (2016). We believe a missed element for shifting from theoretical design to practical implementation is to model, reason about, and support decisions in a dynamic way—and not by using snapshot-based modeling frameworks.
This abstract reports on extending the game-theoretic approach of Yazdanpanah and Yazan (2017) with regulative rules and normative socioeconomic policies—following the successful line of work on normative multi-agent systems Shoham and Tennenholtz (1995); Grossi et al. (2013); Andrighetto et al. (2013). The extension provides a scalable solution to the implementation problem and enables enforcing desired industrial collaborations in a fair and stable manner.
1.1. Research Questions
The following questions guide the design of a game-theoretic framework and its normative coordination mechanism that jointly facilitate the implementation of s:
Games: How to define a game-theoretic basis for s that both reflects their operational cost dynamics and allows the integration of normative rules?
Coordination: How to uniformly represent the regulatory dimension of s using incentive rules and normative policies?
Coordinated Games: How to develop a framework that integrates normative coordination methods into games to enable the fair and stable implementation of desirable s—with respect to an established policy?
Dealing with s’ complex industrial context Yazdanpanah et al. (2016), an ideal implementation platform would be tunable to specific industrial settings, scalable for implementing various topologies, and would not require industries to sacrifice financially nor restrict their freedom in the market. Below, we present the overview of an approach for developing an implementation framework with properties close to the ideal one.
2. Overview of The Approach
As discussed in Albino et al. (2016); Yazdanpanah and Yazan (2017), the total obtainable cost reduction (as an economic benefit) and its allocation among involved firms are key drivers behind the stability of s. For any set of agents involved in an , this value—i.e., the obtainable cost reduction—characterizes the value of the set and hence can be seen as a basis for formulating s as cooperative games. On the other hand, in realistic s, the symbiotic practice takes place in presence of economic, social, and environmental policies and under regulations that aim to enforce the policies by nudging the behavior of agents towards desired ones. This is, while policies generally indicate whether an is “good (bad, or neutral)", the regulations are a set of norms that—in case of agents’ compliance—result in an acceptable spectrum of collective behaviors. We follow this normative perspective and aim to use normative coordination to guarantee the implementability of desirable s—modeled as games—in a stable and fair manner. In the following subsections, we indicate how games can be modeled and coordinated using regulatory incentive rules and normative socioeconomic policies.
2.1. ISNs as Cooperative Games
In the game-theoretic representation of s, the value of any set of agents is defined Yazdanpanah and Yazan (2017) using the difference between the total cost that firms have to pay in case the does not occur, i.e. costs to discharge wastes and to purchase traditional primary inputs (denoted by ), and the total cost that firms have to pay collectively in case the is realized, i.e. costs for recycling and treatment, for transporting resources among firms, and transaction costs (denoted by ). Formally, the among agents in a non-empty finite set of agents is a normalized superadditive cooperative game where for , is equal to if , and otherwise.
Benefit sharing is crucial in the process of implementation, mainly because of stability and fairness concerns. Roughly speaking, firms are rational agents that defect unbeneficial collaborations (instability) and mostly tend to reject relations in which benefits are not shared according to contributions (unfairness). Focusing on the Core and Shapley allocations Osborne and Rubinstein (1994); Mas-Colell et al. (1995)—as standard methods that characterize stability and fairness—these solution concepts appear to be applicable in a specific class of s but are not generally scalable for value allocation in the implementation phase of s. In particular, relying on the balancedness of two-person games, denoted by , we can show that any is implementable in a fair and stable manner. However, in larger games—as balancedness does not hold necessarily—the core of the game may be empty which in turn avoids an implementation that is reasonable for all the involved firms. So, even if a symbiosis could result in collective benefits, it may not last due to instable or unfair implementations. A natural response which is in-line with realistic practices is to employ monetary incentives as a means of normative coordination—to guarantee the implementability of “desired” s. To allow a smooth integration with normative rules, we transform games into basic MC-Nets111A basic MC-Net represents a game in as a set of rules , where , , , , and is the set of rule indices. For a group , a rule is applicable if and . Then will be equal to where denote the set of rule indices that are applicable to . This rule-based representation allows natural integration with rule-based coordination methods and results in relatively low complexity for computing allocation methods such as the Shapley value Lesca et al. (2017); Ieong and Shoham (2005). through the following steps: let be an arbitrary game, be the set of all groups with two or more members where denotes its cardinality. We start with an empty set of MC-Net rules. Then for all groups , for to , we add a rule to the MC-Net.
2.2. Normative Coordination of ISNs
Following Shoham and Tennenholtz (1995); Grossi et al. (2013), we see that norms can be employed as game transformations to bring about more desirable outcomes in games. For this account, given the economic, environmental, and social dimensions and with respect to potential socioeconomic consequences, s can be partitioned in three classes by a normative socioeconomic policy function , where is a finite set of firms. Moreover, , , and are labels—assigned by a third-party authority—indicating whether an is promoted, permitted, or prohibited, respectively.
The rationale behind introducing policies is mainly to make sure that the set of promoted s are implementable in a fair and stable manner while prohibited ones are instable. To ensure this, in real practices, the regulatory agent introduces monetary incentives, i.e., ascribes subsidies to promoted and taxes to prohibited collaborations. We follow this practice and employ a set of rules to ensure/avoid the implementability of desired/undesired s by allocating incentives222See Meir et al. (2011); Zick et al. (2013) for similar approaches on incentivizing cooperative games.. Such a set of incentive rules can be represented by an MC-Net in which is the set of rule indices. Then, the incentive value for , is defined as where denotes the set of rule indices that are applicable to . It is provable that for any game there exists a set of incentive rules to guarantee its implementability.
2.3. Coordinated ISN Games
Having policies and regulations, we integrate them into games and introduce the concept of Coordinated s (s). Formally, let be an and be a set of regulatory incentive rules, both as MC-Nets among agents in . Moreover, for each group , let and denote the value of in and the incentive value of in , respectively. We say the Coordinated Game () among agents in is a cooperative game where for each group , we have that .
It can be observed that employing such incentive rules is effective for enforcing socioeconomic policies. In particular, we have that for any promoted game, under a policy , there exist an implementable game. Analogously, similar properties hold while avoiding prohibited s or allowing permitted ones. The presented approach for incentivizing s is advisable when the policy-maker is aiming to ensure the implementability of a promoted in an ad-hoc way. In other words, an that ensures the implementability of a promoted may ruin the implementability of another promoted . To avoid this, the set of collaborations that a policy marks as promoted should be mutually exclusive. Accordingly, we have the desired result that the mutual exclusivity condition is sufficient for ensuring the implementability of all the s among -promoted groups in a fair and stable manner.
3. Concluding Remarks
The details of the components for developing the implementation framework—rooted in cooperative games and coordinated with normative rules—consist of algorithms for generating incentive rules and policy properties to ensure the implementability of promoted s. We plan to explore the possibility of having multiple policies and tools for policy option analysis Mehryar et al. (2017) in s. Then, possible regulation conflicts can be resolved using prioritized rule sets (inspired by formal argumentation theory Modgil and Prakken (2013); Kaci and van der Torre (2008)). We also aim to focus on administration of s by modeling them as normative multi-agent organizations Boissier and Van Riemsdijk (2013); Yazdanpanah et al. (2016) and relying on norm-aware frameworks Dastani et al. (2016); Aldewereld et al. (2007) that enable monitoring organizational behaviors.
The project leading to this work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680843.
- Albino et al. (2016) Vito Albino, Luca Fraccascia, and Ilaria Giannoccaro. 2016. Exploring the role of contracts to support the emergence of self-organized industrial symbiosis networks: an agent-based simulation study. Journal of Cleaner Production 112 (2016), 4353–4366.
- Aldewereld et al. (2007) Huib Aldewereld, Frank Dignum, Andrés García-Camino, Pablo Noriega, Juan Antonio Rodríguez-Aguilar, and Carles Sierra. 2007. Operationalisation of norms for electronic institutions. In Coordination, Organizations, Institutions, and Norms in Agent Systems II. Springer, 163–176.
- Andrighetto et al. (2013) Giulia Andrighetto, Guido Governatori, Pablo Noriega, and Leendert WN van der Torre. 2013. Normative multi-agent systems. Vol. 4. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik.
- Boissier and Van Riemsdijk (2013) Olivier Boissier and M Birna Van Riemsdijk. 2013. Organisational reasoning agents. In Agreement Technologies. Springer, 309–320.
- Chertow (2000) Marian R Chertow. 2000. Industrial symbiosis: literature and taxonomy. Annual review of energy and the environment 25, 1 (2000), 313–337.
- Dastani et al. (2016) Mehdi Dastani, Leendert van der Torre, and Neil Yorke-Smith. 2016. Commitments and interaction norms in organisations. Autonomous Agents and Multi-Agent Systems (2016), 1–43.
- Grossi et al. (2013) Davide Grossi, Luca Tummolini, and Paolo Turrini. 2013. Norms in game theory. In Agreement Technologies. Springer, 191–197.
- Ieong and Shoham (2005) Samuel Ieong and Yoav Shoham. 2005. Marginal contribution nets: a compact representation scheme for coalitional games. In Proceedings of the 6th ACM conference on Electronic commerce. ACM, 193–202.
- Kaci and van der Torre (2008) Souhila Kaci and Leendert van der Torre. 2008. Preference-based argumentation: Arguments supporting multiple values. International Journal of Approximate Reasoning 48, 3 (2008), 730–751.
- Lesca et al. (2017) Julien Lesca, Patrice Perny, and Makoto Yokoo. 2017. Coalition Structure Generation and CS-core: Results on the Tractability Frontier for games represented by MC-nets. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 308–316.
- Lombardi and Laybourn (2012) D Rachel Lombardi and Peter Laybourn. 2012. Redefining industrial symbiosis. Journal of Industrial Ecology 16, 1 (2012), 28–37.
- Mas-Colell et al. (1995) Andreu Mas-Colell, Michael Dennis Whinston, Jerry R Green, et al. 1995. Microeconomic theory. Vol. 1. Oxford university press New York.
- Mehryar et al. (2017) Sara Mehryar, Richard Sliuzas, Ali Sharifi, Diana Reckien, and Martin van Maarseveen. 2017. A structured participatory method to support policy option analysis in a social-ecological system. Journal of Environmental Management 197 (2017), 360–372.
et al. (2011)
Reshef Meir, Jeffrey S.
Rosenschein, and Enrico Malizia.
Subsidies, Stability, and Restricted Cooperation in
Coalitional Games. In
IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011. 301–306.
- Modgil and Prakken (2013) Sanjay Modgil and Henry Prakken. 2013. A general account of argumentation with preferences. Artificial Intelligence 195 (2013), 361–397.
- Osborne and Rubinstein (1994) Martin J Osborne and Ariel Rubinstein. 1994. A course in game theory. MIT press.
- Shoham and Tennenholtz (1995) Yoav Shoham and Moshe Tennenholtz. 1995. On social laws for artificial agent societies: off-line design. Artificial intelligence 73, 1-2 (1995), 231–252.
- Yazan et al. (2016) Devrim Murat Yazan, Vincenzo Alessio Romano, and Vito Albino. 2016. The design of industrial symbiosis: An input–output approach. Journal of cleaner production 129 (2016), 537–547.
- Yazdanpanah and Yazan (2017) Vahid Yazdanpanah and Devrim Murat Yazan. 2017. Industrial Symbiotic Relations as Cooperative Games. In Proceedings of the 7th International Conference on Industrial Engineering and Systems Management (IESM-2017). 455–460.
- Yazdanpanah et al. (2016) Vahid Yazdanpanah, Devrim Murat Yazan, and W Henk M Zijm. 2016. Normative Industrial Symbiotic Networks: A Position Paper. In Multi-Agent Systems and Agreement Technologies. Springer, 314–321.
- Zick et al. (2013) Yair Zick, Maria Polukarov, and Nicholas R. Jennings. 2013. Taxation and stability in cooperative games. In International conference on Autonomous Agents and Multi-Agent Systems, AAMAS ’13, Saint Paul, MN, USA, May 6-10, 2013. 523–530.