Qualitative Models:

Verbal communication is a source of additional information that, correctly treated, reveals interests, preferences and judgments that can be of great value, especially in the absence of other data. The use of techniques based on fuzzy logic, or multi-criteria analysis, allows us to first model and make this knowledge manageable, and then to incorporate it into assessment and decision-making processes.

With our tools, you can gather opinions from experts and stakeholders in a dynamic and intuitive way, incorporate their knowledge and interests into a decision process, and use it to figure-out to what extent their point of views are homogeneous, or which alternatives are more aligned with their preferences.

Cuantitative Complex Models:

Decisions' complexity increase as does the scope of variables, the level of detail required for the solutions, the flexibility needed to build alternatives, or the diversity of interests under consideration. The incorporation of dynamic factors, indirect effects, as well as of surrogated operational models into a comprehensive model, requires customized and advanced modeling and representation techniques.

Our approach allows to display, in an orderly and gradual way, large and complex systems embodying large amount of information at different levels of detail. This methodology allows to check out results's validity, and compare alternatives, and analyze them in detail, from different perspectives, without losing the big picture.

Optimization and Multi-Objective Analysis:

Multi-objective optimization techniques allow to find out compromise solutions simultaneously aiming at multiple objectives. These compromise solutions affords achieving, for each objective, the best possible result without harming others' interests and therefore, from the negotiation standpoint, they are the most efficient aggreement alternative.

Typically, multi-objective optimization methods produce a large set of compromise solutions that, once analyzed, allows balancing trade-offs between interests, and inferring their degree of alignment.

We have developed our methods to delimitate the regions of greatest convergence between interests, which helps focusing the analysis on the most acceptable solutions, and dismiss unrealistic expectations.

Uncertainty Analysis:

The uncertainty analysis consists in evaluating how the variability inherent to the assumptions of a model, influences its outcome.

Monte Carlo Simulation is one of the most common and contrasted techniques in this type of analysis, and consists in performing a large number of simulations by choosing, each time, random values from the PDFs representing the values each input variable (assumption) can take.

The most basic form of uncertainty analysis is the sensitivity analysis, which relates the variation of a variable's values, with the result for a given objective. Our methods, besides, combine variables to build scenarios, identify those in which strategies have a resilient or vulnerable behaviour, and determine the risks and opportunities that each strategy bear for all analysed objectives.

By establishing scales, organizations can systemically and progressively allocate, and regulate, attributions and responsibilities from the upper to the lower levels (top-down control). At the same time, however, each scale's preformance will depend on how its sub-scales works and so on, favoring the appearance of cascading failures that compromise the overall system's (bottom-up propagation) performance.

The relational uncertainty of a system represents the effect that the organization's multi-scale structure may have on the partial and global objectives, and will depend on the configuration of rights (atributions) and duties (responsabilities) that are assigned at each scale. A correct configuration will allow increasing system's resiliece by maximizing the advantages of adaptation capacity at local (Bottom) scale, while minimizing risks for the overall (Top) objectives.

The use of advanced multi-objective optimization, and of uncertainty analysis techniques , enable us to provide the decision makers with a multitude of alternatives, and to generate a large number of possible scenarios. However, the diversity of options can become an important decisional burden, generating noise and hindering both the extraction of knowledge throughout the process, as the selection of alternatives itself.

The joint employment of visual analysis techniques, classifiers and dynamic control systems, allow us to develop methods enabling users to interact with our tools, and dynamically focus the selection of alternatives in the regions of greatest interest, dismissing irrelevant solutions and avoiding unproductive efforts. With our methods it is possible to identify, in an intuitive and dynamic way, the solutions meeting the multiple conditions required to be acceptable.

We also have methods to synthesize the space of solutions. Based on cluster analysis, these methods group the solutions into a manageable number of sub-sets and then, based on the criterion of preference chosen by the decision maker, select representative solutions from each sub-set.