On Interoperable Trust Negotiation Strategies

AbstractThis paper sheds light on Syrian refugee women’s negotiation strategies in language learning classrooms and in their broader social contexts from an intersectional perspective. Drawing on in-depth interviews and focus groups complemented by participatory observation in language classes, we use a post-structuralist approach to examine gendered language socialization. Our research combines an intersectional framework and a Bourdieusian perspective on symbolic capital to show how women perform gender and negotiate their roles in classrooms, within families and vis-à-vis the host society. The findings demonstrate that being a woman and a migrant presents particular challenges in learning language. At the same time, learning language allows for the re-negotiation of gender relations and power dynamics. We find that gender structures women’s access to linguistic resources and interactional opportunities as they perform language under social pressure to conform to prescribed roles as mothers, wives and virtuous, and shy women. Yet, these roles are not static: gender roles are also reconstituted in the process of language learning and gaining symbolic capital.

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ANEGMA: an automated negotiation model for e-markets

Autonomous Agents and Multi-Agent Systems ◽

10.1007/s10458-021-09513-x ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Pallavi Bagga ◽

Nicola Paoletti ◽

Bedour Alrayes ◽

Kostas Stathis

Keyword(s):

Reinforcement Learning ◽

Deep Neural Network ◽

Automated Negotiation ◽

Learning To Learn ◽

Negotiation Model ◽

Negotiation Strategies ◽

Exploration Time ◽

Model Free ◽

Bilateral Negotiations ◽

Time Required

AbstractWe present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

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The Moscow Theater Hostage Crisis: The Perpetrators, their Tactics, and the Russian Response

International Negotiation ◽

10.1163/1571806031310798 ◽

2003 ◽

Vol 8 (3) ◽

pp. 577-611 ◽

Cited By ~ 17

Author(s):

Richard Pilch ◽

Adam Dolnik

Keyword(s):

International Debate ◽

Negotiation Strategies ◽

Crisis Negotiation ◽

Hostage Crisis ◽

The Future ◽

Analytical Perspective ◽

Potential Impact ◽

Media Event ◽

Successes And Failures

AbstractThe Moscow theater hostage crisis was a spectacular media event, which sparked a wide domestic and international debate concerning the appropriateness of the Russian response. This article attempts to reconstruct and assess the events that took place in terms of negotiability of the incident, and seeks to provide an analytical perspective on the possible alternatives that were available to the Russian authorities throughout the crisis. Part I provides a brief overview of the events that unfolded. This section of the article also places Chechen motivations behind the incident into perspective with regard to past Chechen operations and to their overall strategy. Part II focuses on the details of the attack itself, particularly the Russian response. Special attention is devoted to analyzing the successes and failures of both the negotiations and the tactical assault. The conclusion discusses the implications of the Moscow theater incident for the future, including its potential impact on the likelihood of success of crisis negotiation strategies and the future tactics of the Chechen rebels.

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