A Generalized Belief Entropy With Nonspecificity and Structural Conflict

2021 ◽  
pp. 1-14
Author(s):  
Mi Zhou ◽  
Shan-Shan Zhu ◽  
Yu-Wang Chen ◽  
Jian Wu ◽  
Enrique Herrera-Viedma
Keyword(s):  
Belief Entropy ◽  
Structural Conflict

Ethnocratic Policies and Indigenous Resistance: Bedounin Arabs and the Israeli Settler State

2003 ◽  
Vol 1 (2) ◽  
pp. 161-191 ◽  
Author(s):  
Oren Yiftachel
Keyword(s):  
Indigenous Peoples ◽  
Nation Building ◽  
Arab Population ◽  
Land Planning ◽  
Indigenous Resistance ◽  
Development Policies ◽  
Economic Strategies ◽  
Basic Services ◽  
The Impact ◽  
Structural Conflict

This article examines the evolving relations between Israel and the indigenous Bedouin Arab population of the southern Beer-Sheba region. It begins with a discussion of theoretical aspects, highlighting a structural conflict embedded in the ‘ethnocratic’ nature of nation-building typical of ‘pure’ settler states, such as Israel. The place of the Bedouin Arab community is then analyzed, focusing on the impact of one of Israel's central policies—the Judaization of territory. The study traces the various legal, planning and economic strategies of Judaizing contested lands in the study area. These have included the nationalization of Arab land, the pervasive establishment of Jewish settlements, the forced urbanization of the Bedouin Arabs, and the denial of basic services to Bedouins who refuse to urbanize. However, the analysis also finds a growing awareness among indigenous Arabs of their being discriminated against on ethnic grounds, and the emergence of effective resistance. In recent years, this has resulted in a deadlock between state authorities and the indigenous peoples. The case of the Bedouin Arabs demonstrates that the ethnocentric settler state is weakening and fragmenting, partially at least, due to its own expansionist land, planning and development policies.

Overcoming the Paradox of Conflict Prevention

2018 ◽  
Author(s):  
Edward Newman ◽  
Eamon Aloyo
Keyword(s):  
Violence Prevention ◽  
Social Economic ◽  
Driving Forces ◽  
Crisis Response ◽  
Warning Signs ◽  
Violent Conflict ◽  
Policy Implications ◽  
Conflict Prevention ◽  
Political Factors ◽  
Structural Conflict

Progress in conflict prevention depends upon a better understanding of the underlying circumstances that give rise to violent conflict and mass atrocities, and of the warning signs that a crisis is imminent. While a substantial amount of empirical research on the driving forces of conflict exists, its policy implications must be exploited more effectively, so that the enabling conditions for violence can be addressed before it occurs. Violence prevention involves a range of social, economic, and political factors; the chapter highlights challenges—many of them international—relating to deprivation, inequality, governance, and environmental management. Prevention also requires overcoming a number of acute political obstacles embedded within the values and institutions of global governance. The chapter concludes with a range of proposals for structural conflict prevention and crisis response, as well as the prevention of mass atrocities.

On cumulative belief entropy

2021 ◽  
Author(s):  
Huizi Cui ◽  
Bingyi Kang
Keyword(s):  
Belief Entropy

scholarly journals A New Uncertainty Measure in Belief Entropy Framework

2018 ◽  
Vol 9 (11) ◽  
Author(s):  
Moise Digrais Mambe ◽  
Tchimou N’Takp´e ◽  
Nogbou Georges ◽  
Souleymane Oumtanaga
Keyword(s):  
Uncertainty Measure ◽  
Belief Entropy

scholarly journals Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 993 ◽  
Author(s):  
Bin Yang ◽  
Dingyi Gan ◽  
Yongchuan Tang ◽  
Yan Lei
Keyword(s):  
Data Fusion ◽  
Incomplete Information ◽  
Information Fusion ◽  
Evidence Theory ◽  
Sensor Data ◽  
Uncertain Information ◽  
Open World ◽  
Belief Entropy ◽  
Open World Assumption ◽  
Deng Entropy

Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

scholarly journals A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 163 ◽  
Author(s):  
Qian Pan ◽  
Deyun Zhou ◽  
Yongchuan Tang ◽  
Xiaoyang Li ◽  
Jichuan Huang
Keyword(s):  
Evidence Theory ◽  
Mass Function ◽  
Probability Mass Function ◽  
Main Work ◽  
Basic Probability ◽  
Open Issue ◽  
Belief Entropy ◽  
Probability Mass ◽  
Hartley Entropy ◽  
Theory Framework

Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

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