Situation forecasting and decision-making optimization based on using markov finite chains for areas with industrial pollutions

Decision Making of Negotiation Agents Using Markov Chains

In dynamic and complex negotiation environments, a negotiation agent can participate or quit negotiation at any time and can reach an agreement with more than one trading partner as the result of the existence of dynamic outside options. Thus, it’s important for a negotiation agent to make a decision on when to complete negotiation given market dynamics. Rather than explicitly modelling all the...

Simulation-Based Optimization Algorithms for Finite-Horizon Markov Decision Processes

We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of these algorithms are developed for finite state and compact action spaces while the other two are for finite state and finite action spaces. Of the former two, one algorithm uses a linear parameterization for the policy, resulting in reduced memory complexity. Convergence analysis is briefly sketche...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Lectures on finite Markov chains

Benchmark Forecasting in Data Envelopment Analysis for Decision Making Units

Although DEA is a powerful method in evaluating DMUs, it does have some limitations. One of the limitations of this method is the result of the evaluation is based on previously data and the results are not proper for forecasting the future changes. So For this purpose, we design feedback loops for forecasting inputs and outputs through system dynamics and simulation. Then we use DEA model to f...