THE EFFECT OF COMPETENCY, NEGOTIATION MODEL AND EMOTIONAL INTELLEGENCE IN THE STAKEHOLDERS CAPABILITY ON RESULT OF NEGOTIATION CONSTRUCTION DISPUTE IN INDONESIA

This research aim to test do emotional intellegence consisting of five component that is recognition self awareness, self regulation, motivation, empathy, and social skills have an effect on to storey level understanding of accountancy point of view from gender perspective. This research also aim to know the existence of role self confidence as moderating variable to emotional intellegence influence to storey level understanding of accounting. Besides also this research aim to see the existence of difference emotional intellegence between student owning self confidence of strong with student which is self confidence of weak. Measuring instrument to measure storey level understanding of accountancy is average point of accountancy that is PA1, PA2, AKM1, AKM2, AKL1, AKL2, AU1, AU2, and TA. The data analysis used is simple linear regression, Moderating Regression Analysis (MRA), and independent sample t-test. The results show that recognition self awareness, self regulation, motivation, social skill and empathy do not have an effect on by significance and only empatht has role as quasi moderator variable. There is no difference between emotional intellegence woman and man. But, weak self confidence and strong self confidence differ for all of emotional intellegence (recognition self awareness, self regulation, motivation, empathy, and social skills). Many factors which influence storey level understanding of accountancy like mental stress factor, and so on. Result of this research can give contribution to university in order to compiling curricullum and give input to student in order to develop and manage their emotional intellegence and self confidence.

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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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