Multiple Interdependent Issues Research Articles

How the qualities of actor-issue interdependencies influence collaboration patterns

Environmental governance is complex because it addresses challenges anchored in different sectors and concerns multiple interdependent issues. Managing those complex interdependencies through collaboration is vital for efficient long-term environmental governance. However, because interdependencies between environmental issues are challenging to unravel and vastly complex, it is challenging for actors to account for them when deciding with whom to collaborate. I use the concept of social-ecological networks to study interdependencies among actors and environmental issues and ask how the quality of actor-issue interdependencies influences collaboration patterns. Based on the actor-issue network, I account for interdependencies based on three distinct qualities of actor-issue paths, i.e., (i) length of actor-issue paths: how closely actors are connected by environmental issues, (ii) multiplexity of actor-issue paths: if actors have multiple parallel paths connecting them through environmental issues, and (iii) similarity of actor-issue paths: whether actors’ environmental impact is similar to one of their potential collaboration partners. Using exponential random graph models and data on eight Swiss wetlands, a qualitative meta-regression analysis of the results reveals that the three qualities of actor-issue interdependencies influence collaboration patterns between actors. Whether the impact of actor-issue interdependencies on the probability of collaboration ties is positive or negative largely depends on the complexity of the governance situations. Only in situations with homogeneous case areas and under the absence of borders (low network exogenous governance complexity) as well as in the presence of many actors do the length, multiplexity, and similarity of actor-issue interdependencies have a clear, positive impact on the formation of collaboration ties. Although the comparative setting helps identify specific governance settings where the hypotheses are supported, it also reveals the importance of multi-case studies to compare contextual differences between cases.

Compromising Strategies for Agents in Multiple Interdependent Issues Negotiation

Compromising Strategies for Agents in Multiple Interdependent Issues Negotiation

Effective Automated Negotiation Based on Issue Dendrograms and Partial Agreements

Negotiation is both an important topic in multi-agent systems research and an important aspect of daily life. Many real-world negotiations are complex and involve multiple interdependent issues, therefore, there has been increasing interest in such negotiations. Existing nonlinear automated negotiation protocols have difficulty in finding solutions when the number of issues and agents is large. In automated negotiations covering multiple independent issues, it is useful to separate out the issues and reach separate agreements on each in turn. In this paper, we propose an effective approach to automated negotiations based on recursive partitioning using an issue dendrogram. A mediator first finds partial agreements in each sub-space based on bids from the agents, then combines them to produce the final agreement. When it cannot find a solution, our proposed method recursively decomposes the negotiation sub-problems using an issue dendrogram. In addition, it can improve the quality of agreements by considering previously-found partial consensuses. We also demonstrate experimentally that our protocol generates higher-optimality outcomes with greater scalability than previous methods.

A Contract Negotiation Model for Constituent Systems in the Acquisition of Acknowledged System of Systems

In the acquisition of acknowledged system of systems (SoSs), the SoS manager attempts to reach a coalition agreement with each of selected constituent systems. When the systems have self-interests that are incompatible with the SoS’s goal, contract negotiation is a way of resolving conflicts. Complexity of the acknowledged SoS acquisition makes the negotiation a hard decision process for constituent systems. To the SoS manager, the negotiation with consistent systems is difficult too, considering that the behavior and strategies of constituent systems in negotiation impact the ability and timeliness of a group to agree on an SoS architecture, and so the overall mission effectiveness of the SoS. Motivated by these, this paper develops a contract negotiation model for constituent systems in the acknowledged SoS acquisition. The model consists of: 1) a protocol for negotiating multiple interdependent issues of multiple items over multiple stages and 2) a decision framework of constituent systems. The model is not only a decision support for constituent systems but also a tool with which the SoS manager can better understand constituent systems.

Efficient issue-grouping approach for multiple interdependent issues negotiation between exaggerator agents

Many real-world negotiations involve multiple interdependent issues, which makes an agent's utility functions complex, with nonlinear shapes and multiple optima. Traditional negotiation mechanisms were designed for linear utilities, and do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; it can be extremely difficult to find high-quality solutions when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the contract space into several largely independent sub-spaces. In this paper, we propose a method based on this concept. A mediator finds sub-contracts in each sub-space based on votes from the agents, and combines the sub-contracts to produce the final agreement. We demonstrate, experimentally, that our protocol allows high-optimality outcomes with greater scalability than previous efforts. We also demonstrate a method for addressing the potential problem of strategic non-truthful voting by the agents.

MO‐A‐WAB‐01: MRI‐Guided Radiation Therapy

The concept of integrating a magnetic resonance imaging (MRI) scanner with a therapy radiation source, either a therapy linear accelerator (linac) or 60 Co, emerged as a feasible novel approach for MRI‐guided radiation therapy (i.e. MRIgRT or MRgRT) in recent years. The main motivation for the technological development of such radiotherapy systems is to provide state of the art MR imaging inside the treatment room to guide the treatment delivery. MRI offers the capability to visualize soft‐tissue as well as to perform physiological assessment of healthy and tumor tissues. The aim of MRIgRT is to facilitate adaptive radiotherapy by using on demand MRI data to update and personalize patient treatments.The MRI‐linac/ 60 Co system integration is a challenging task due to the intrinsic default incompatibility between the sub‐components. Multiple interdependent issues need to be resolved in order to achieve the optimal operation of the MRI scanner and the radiation source(s) such as: a) radiofrequency (RF) interference, b) magnetic field coupling, c) perturbation of the dose deposited in tissue due to the presence of an external magnetic field, and d) escalation of patient skin dose. This translates into solving a complex optimization problem which drives the overall system architecture. The session will provide an overview of the MRI‐guided radiation therapy systems. The speakers will present the specifics of their proposed designs and the latest technological developments (hardware and software). They will also discuss key aspects related to the clinical implementation of their systems such as safety, applications, workflows, quality control, staffing models for supporting the infrastructure, and preliminary data.Learning Objectives:1. Understand the main concepts of MRI‐guided radiation therapy;2. Understand the issues and proposed solutions related to the integration of MRI‐guided radiotherapy systems;3. Understand the advantages and limitations of MRI‐guided radiotherapy systems.

AN APPROACH TO SCALABLE MULTI‐ISSUE NEGOTIATION: DECOMPOSING THE CONTRACT SPACE

Most real‐world negotiation involves multiple interdependent issues, which makes an agent’s utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they cannot find a high‐quality solution when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent subspaces. In this paper, we propose a method for decomposing a utility space based on every agent’s utility space. In addition, the mediator finds the contracts in each group based on the votes from all agents, and combines the contract in each issue‐group. This method allows good outcomes with greater scalability than the method without issue‐grouping. We demonstrate that our protocol, based on issue‐groups, has a higher optimality rate than previous efforts, and discuss the impact on the optimality of the negotiation outcomes.

Special Issue on ‘Human Factors and Computational Models in Negotiation’

Negotiationisacomplexemotionaldecision-makingprocessaimingtoreachanagreementtoexchange goods orservices.Although adailyactivity,fewpeople areeffective negotiators. Existing support systems make a significant improvement if the negotiation space is well-understood, because computers can better cope with the computational complexity. However, the negotiation space can only be properly developed if the human parties jointly explore their interests. The inherent semantic problem and the emotional issues involved make that negotiation cannot be handled by artificial intelligencealone,andahuman-machinecollaborativesystemisrequired.Everyoneis an experienced negotiator and everyone has an opinion about their negotiation skills. However, even professional negotiators can still improve their skills considerably by using automated support for negotiation. Negotiation is a prime example of a task for which the human mind is but partially equipped, and for which artificial intelligence can only provide partial assistance. Computational power, data storage, search techniques, computational heuristics to tackle exponential problem spaces, are among the good products of AI. However, AI has not solved the problem of the huge amount of knowledge necessary to cope with, and understand arbitrary conversations and problems. The complexity and the variability of the problems humans wish to address are just too much to handle. It thus becomes important to both study the human factors as well as computational models that contribute to reaching negotiated agreements between parties. This special issue is a selection of papers that were presented at the First InternationalConferenceonHumanFactorsandComputationalModelsinNegotiation.These papers present various research goals, ideas, challenges and approaches towards creating the next generation of negotiation support systems. The first paper ‘A Secure and Fair Protocol for Multiple Interdependent Issues Negotiation’ focuses on negotiation with multiple interdependent issues in which

Efficient Issue-Grouping Approach for Multi-Issues Negotiation between Exaggerator Agents

Most real-world negotiation involves multiple interdependent issues, which makes an agent's utility functions complex. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; it can be extremely difficult to find high-quality solutions when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the contract space into several largely independent sub-spaces. In this paper, we propose a method for decomposing a contract space into sub-spaces based on the agent's utility functions. A mediator finds sub-contracts in each sub-space based on votes from the agents, and combines the sub-contracts to produce the final agreement. We demonstrate, experimentally, that our protocol allows high-optimality outcomes with greater scalability than previous efforts. We also address incentive compatibility issues. Any voting scheme introduces the potential for strategic non-truthful voting by the agents, and our method is no exception. For example, one of the agents may always vote truthfully, while the other exaggerates so that its votes are always "strong." It has been shown that this biases the negotiation outcomes to favor the exaggerator, at the cost of reduced social welfare. We employ the limitation of strong votes to the method of decomposing the contract space into several largely independent sub-spaces. We investigate whether and how this approach can be applied to the method of decomposing a contract space.

A Recursive Protocol for Negotiating Contracts Under Non-monotonic Preference Structures

Negotiating contracts with multiple interdependent issues may yield non-monotonic preference spaces for the participating agents. These negotiations are specially challenging because of the complexity and dimension of the search space. Automated negotiation mechanisms designed and proven useful for monotonic utility spaces may fail in these negotiation scenarios. This paper presents a novel solution to the problem of automated multi-issue negotiations in the context of complex utility spaces. We seek to address the challenge of intractably large contract spaces and utility functions with multiple local optima in automated negotiation scenarios. A protocol for automated bilateral multi-attribute negotiation processes is proposed, in which the individual agents’ preferences can be non-monotonic and discontinuous. The protocol is based on a recursive non-mediated bargaining mechanism, which involves two agents who simultaneously exchange proposals defined as regions within the negotiation space. An agreement on a region implies a new bargaining which is restricted to that region. This recursive process is governed by a set of rules which modulate the joint exploration of the negotiation space until an agreement is found or a deadline expires. The protocol is experimentally evaluated under monotonic and non-monotonic preference scenarios, confirming that the protocol is able to produce outcomes close to the Pareto frontier in acceptable negotiation time, outperforming previous approaches.

Common Testbed Generating Tool Based on XML for Multiple Interdependent Issues Negotiation Problems

Multiple interdependent issues negotiations have been widely studied since most real-world negotiation involves multiple interdependent issues. Our work focuses on negotiation with multiple interdependent issues in which agent utility functions are nonlinear. In the field of multiple issue negotiations, there are no established common testbeds for evaluating protocols. In this paper, we propose a common testbed creating tool based on XML that mainly covers the utility functions based on cube-constraints and cone-constraints. First, we propose a testbed generating tool that inputs configuration data and outputs XML formatted files that represent agent utility spaces. The current tool can produce four types of utility spaces: Random, A Single Hill, Two-Hills, and SeveralHills. These types are observed in real negotiation settings. Also we define the agent’s utility space information based on XML formats. By defining the testbed data as XMLs, users can easily read the files and change the data structure.

Scalable and Efficient Protocols by Grouping Issues in Multiple Interdependent-Issue Negotiations

Most real-world negotiation involves multiple interdependent issues, which create agent utility functions that are nonlinear. Our research focuses on developing algorithms that enable this kind of negotiation. We present a novel bidding-based negotiation protocol that addresses the excessively high failure rates that existing approaches face when applied to highly complex nonlinear utility functions. This protocol works by using issue dependency information as follows. First, agents generate an interdependency graph by analyzing the agent's constraints. Second, a mediator identifies issue-groups based on the agents' interdependency graphs. Third, agents generate bids that are divided into these issue-groups. Finally, the mediator identifies the winning contract by finding the best combinations of bids in each issue-group. In this paper, we demonstrate that our proposed protocol is highly scalable when compared to previous efforts in a more realistic experimental setting.

Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents' utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield individual and social welfares. In scenarios involving highly uncorrelated utility spaces, low social welfare usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.

A generic coordination mechanism for lot-sizing in supply chains

A new generic mechanism to coordinate decentral planning of a group of independent and self-interested decision makers, who are searching for an agreeable contract regarding multiple interdependent issues, in the case of asymmetric information is presented. The basic idea of the mechanism is that the group members cooperatively carry out an evolutionary search in the contract space. Therefore the (1,?)-selection procedure, which is used in many evolutionary strategies, is combined with the Borda maximin voting rule, which has been applied successfully in group decision making. The proposed mechanism is realized, applied and evaluated for production coordination in a supply chain. A decentralized variant of the multi-level uncapacitated lot-sizing problem (MLULSP) is taken as the production model. For the evaluation 95 problem instances are generated based on MLULSP instances taken from the literature, with problem sizes varying from 5 to 500 items, from 12 to 52 periods. Experimental results show that the proposed mechanism is effective to determine fair cost distributions.

A Secure and Fair Protocol that Addresses Weaknesses of the Nash Bargaining Solution in Nonlinear Negotiation

Negotiation with multiple interdependent issues is an important problem since much of real-world negotiation falls into this category. This paper examines the problem that, in such domains, agent utility functions are nonlinear, and thereby can create nonconvex Pareto frontiers. This in turn implies that the Nash Bargaining Solution, which has been viewed as the gold standard for identifying a unique optimal negotiation outcome, does not serve that role in nonlinear domains. In nonlinear domains, unlike linear ones, there can be multiple Nash Bargaining Solutions, and all can be sub-optimal with respect to social welfare and fairness. In this paper, we propose a novel negotiation protocol called SFMP (the Secure and Fair Mediator Protocol) that addresses this challenge, enabling secure multilateral negotiations with fair and pareto-optimal outcomes in nonlinear domains. The protocol works by (1) using nonlinear optimization, combined with a Multi-Party protocol, to find the Pareto front without revealing agent’s private utility information, and (2) selecting the agreement from the Pareto set that maximizes a fair division criterion we call approximated fairness. We demonstrate that SFMP is able to find agreements that maximize fairness and social welfare in nonlinear domains, and out-performs (in terms of outcomes and scalability) previously developed nonlinear negotiation protocols.

Secure and Scalable Protocols for Multiple Issues Negotiations

Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with multiple interdependent issues in which agent utility functions are nonlinear. Existing works have not yet concerned with agents' private information that should be concealed from others in negotiations. In this paper, we propose Distributed Mediator Protocol and Take it or Leave it Protocol for negotiation that can reach agreements and completely conceal agents' private information. Moreover, we propose Hybrid Secure Protocol that combines Distributed Mediator Protocol with Take it or Leave it Protocol. The Hybrid Secure Protocol can also reach agreements while completely concealing agents' private information. Furthermore, the Hybrid Secure Protocol achieves high optimality and uses less communication cost. We demonstrate that Hybrid Secure Protocol can improve communication cost more than DMP with high optimality.

Secure and efficient protocols for multiple interdependent issues negotiation

Multi-issue negotiation protocols represent a promising field sincemost negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with multiple interdependent issues in which agent utility functions are nonlinear. Firstly, we define utility function based on cone-constraints which are nonlinear. The utility function based on cone-constraint is more realistic than existing utility models and configures the risk attitudes to the cone-constraint. However, if the utility function has cone-constraint features, the utility space becomes extremely nonlinear, making it very difficult to find the optimal agreement point. Existing works have not yet concerned with agents' private information that should be concealed from others in negotiations. In this paper, we propose Distributed Mediator Protocol and Take it or Leave it Protocol for negotiation that can reach agreements and completely conceal agents’ private information. Moreover, we propose Hybrid Secure Protocol that combines Distributed Mediator Protocol with Take it or Leave it Protocol. The Hybrid Secure Protocol can also reach agreements while completely concealing agents’ private information. Furthermore, the Hybrid Secure Protocol achieves high optimality and uses less communication cost. We demonstrate the performance of Hybrid Secure Protocol in cone-constraints and cube-constraints situations.

Using Autonomous Threshold Adjustment to Enable Multi-Party Negotiations with Multiple Interdependent Issues

Using Autonomous Threshold Adjustment to Enable Multi-Party Negotiations with Multiple Interdependent Issues

Using Iterative Narrowing to Enable Multi-Party Negotiations with Multiple Interdependent Issues

Multi-issue negotiations are a central part of many coordination challenges, and thus represent an important research topic. Almost all previous work in this area has assumed that negotiation issues are independent, but this is rarely the case in real-world contexts. Our work focuses on negotiation with interdependent issues and, therefore, nonlinear (multi-optimum) agent utility functions. Since the utility functions are typically very complex, the challenge becomes finding high-quality negotiation outcomes without making unrealistic demands concerning how much agents reveal about their utilities. Since negotiations often involve more than two parties, the approach should also be scalable. In this paper, we propose a novel protocol for addressing these challenges, wherein agents approach agreements by iteratively narrowing the space of possible agreements. In the early stages, agents submit rough bids representing promising regions from their utility functions. In later stages, they submit increasingly narrow bids for the subset of those regions that the negotiating parties all liked. We show that our method outperforms existing methods in large nonlinear utility spaces, and is computationally feasible for negotiations with as many as ten agents.

Negotiating Complex Contracts

Work to date on computational models of negotiation has focused almost exclusively on defining contracts consisting of one or a few independent issues and tractable contract spaces. Many real-world contracts, by contrast, are much more complex, consisting of multiple inter-dependent issues and intractably large contract spaces. This paper describes a simulated annealing based approach appropriate for negotiating such complex contracts that achieves near-optimal social welfares for negotiations with binary issue dependencies.