Existing literature in group-decision making proposed various rules of aggregating individuals’ opinions to group outcomes.With anonymity maintained, this paper can model round-robin assessments by a group with individuals updating their assessments every round in a Bayesian manner as per Bordley (1983, 1986, 2009). Utilizing the properties of the finite Markov Chain process, the analysis shows (a) the conditions for a group consensus to converge, (b) the maximum number of rounds before such convergence occurs, and (c) the consensus assessment. The resulting dynamic model is tested to show that it also captures the results of several empirical studies. We apply them to the negotiationfor the transboundary dispute and our simulations demonstrate the convergence of three different cases of lower possibilities, which support transboundary cases and resolutions. We also develop algorithms based on Fuzzy Delphi (Murray et al. 1985, Ishikawa et al. 1993) and Grey Delphi Methods (Ma et al., 2011) to predict the probability and likely outcomes of the transboundary dispute between China and India, which is one of the cases with low probability. Upon 1,000 simulations under volatile international relations, the development of theconvergence demonstrates the integrated Delphi Method is more suitable for predicting volatile situations.
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This research is partly supported by VC Research (VCR 0000019).
- Bayesian updating
- Dynamic assessment model
- Finite Markov chain process
- Fuzzy Delphi and Grey Delphi methods
- Group decision making
- Negotiation with lower possibilities
- Transboundary disputes