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(…) There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns: these are things we don't know we don't know (…)“

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Our Approach

aftermath follows a unique consulting approach that promotes the interaction of the consultant, the client and the academic researcher. We address our projects according to the limits of our knowledge and try to go as deep into the matter as conceptually feasible but letting pragmatism drive our work.

We define three different consulting situations, each one requiring a particular approach:

  • Situations where client and consultant handle ‘KNOWN KNOWNS’ in the standard format of a plain consultancy work.

  • Other times, client and consultant face KNOWN UNKNOWNS’:  there is an indentified problem but no feasible tools available to address it. It is then when we involve the modeller and the programmer to develop tailor made software appliances to be used at the consultancy work.

  • Occasionally, the problem is fundamentally more complex, unchartered or its analysis unfeasible with the means and knowledge currently available. It is when we face these ‘UNKNOWN UNKWOWNS’ (Knightian uncertainity)- when we implement our academic line by creating different research task-forces at top universities world wide that help us tackling the problem from a different angle and deploying the newest modelling techniques.

Being capable to exploit many times more interactive information channels among company, industry and academy members as well as having a direct line to frontier-academic-knowledge confers us an unchallenged competitive advantage over our peer colleagues.

The technology and the approach.

Mathematical consulting requires a high literacy on analytic methodologies and command on computing skills. Being a heterogeneous group of researchers and consultants, we master a wide range of methods. A sample of these would be: Bayesian methods, optimization meta-heuristics, graphical models, cluster analysis, neural networks, information retrieval, machine learning, Markov chains, stochastic analysis, social network analysis, recommender systems, collaborative filtering, multivariate statistics, non parametric statistics, state space models, time series, survival analysis, dynamic optimization, semi algebraic space analysis, group theory, qualitative similitude analysis, fuzzy logic, causal models, etc. The final result usually involves a mix of these approaches and great amount of home grown techniques inspired by the specific problem at hand.

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aftermath, Spain.
(aftermath research).
Pedro Muguruza 6
28036, Madrid
Spain@after-math.es
aftermath, Germany.
(WIWEX Gmbh)
Spandauer St. 1
D-10178, Berlin
Germany@after-math.es

aftermath USA
(Methodic Solutions)
1982 N Decatur Rd
Atlanta, GA 30307
USA@after-math.es