Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability

With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

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Households Debt Behavior and Financial Instability: Towards an Agent-Based Model with Experimentally Estimated Behavioral Rules

Advances in Intelligent Systems and Computing - Distributed Computing and Artificial Intelligence, 11th International Conference ◽

10.1007/978-3-319-07593-8_12 ◽

2014 ◽

pp. 91-98

Author(s):

Paola D’Orazio

Keyword(s):

Financial Instability ◽

Agent Based Model ◽

Agent Based ◽

Behavioral Rules ◽

Debt Behavior

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Label Enhancement for Label Distribution Learning via Prior Knowledge

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/446 ◽

2020 ◽

Author(s):

Yongbiao Gao ◽

Yu Zhang ◽

Xin Geng

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Emotion Recognition ◽

Prior Knowledge ◽

Decision Process ◽

Age Estimation ◽

State Of The Art ◽

Learning Agent ◽

Label Distribution Learning ◽

Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to an instance. However, most of training datasets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. We propose to use the prior knowledge to recover the label distributions. The process of recovering the label distributions from the logical labels is called label enhancement. In this paper, we formulate the label enhancement as a dynamic decision process. Thus, the label distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

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Theory, Data, and Methods

Handbook of Research on Digital Research Methods and Architectural Tools in Urban Planning and Design - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-5225-9238-9.ch009 ◽

2019 ◽

pp. 156-202

Author(s):

Eda Ustaoglu ◽

Arif Çagdaş Aydinoglu

Keyword(s):

Machine Learning ◽

Land Use ◽

Information System ◽

Land Use Change ◽

Model Development ◽

Agent Based ◽

Change Models ◽

Multi Scale ◽

Land Use Dynamics ◽

Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

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Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

Developing Advanced Web Services through P2P Computing and Autonomous Agents - Advances in Web Technologies and Engineering ◽

10.4018/978-1-61520-973-6.ch003 ◽

2010 ◽

pp. 36-48 ◽

Cited By ~ 1

Author(s):

Tarek Helmy

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Machine Learning Algorithms ◽

Agent Based ◽

Detection Model ◽

Detection Analysis ◽

Proposed Model ◽

Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

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