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Research

Disruption by Foundational Research at the Interfaces of Economics, Computer Science, Mathematics and Sociology

 

The problem addressable by the OiCOS technology originated 2000 in Viktor's PhD in economics. The question was: "Which group of about 30 western, middle and eastern European countries is suited to form a monetary union?" The models were formulated in hundreds of nonlinear stochastic forward looking difference equations. To check the models and to derive a recommendation about 60 thousands macro- and microeconomic variables in time series from 1950 had to be processed by code written in mixtures of Fortran, C++ and Matlab and run on large parallel computer clusters.

But the real problems arose in the aftermaths of the meetings with the  PhD advisor: He asked for seemingly simple adjustments of the questions to ask the data which repeatedly resulted in months of tedious, error prone and manual reorganisations of models, data and code.

Similar problems arise when complex models in industry have to be built when designing, running and reorganising virtual power plants, industrial production sites or complex banking products.

How to automate, simplify and make the modelling and programming process of social systems cheaper, more reliably structured and organised?

The problem, now understood and at the core of OiCOS know-how, was the non-compositionality of economic models. By compositionality we mean decidability and programmability by programs and metaprogramms. Our understanding is that the industrial revolution in machines is the result of compositional methods in physics, mathematics and engineering also from understanding of compositionality for software architectures. But economics, business models and human organisations can not be designed so far by parts in teams of specialised experts and composed into an overall economic or business model, organigram, process flow diagram or economic architecture or model in a software on top of some real physical exchange with simulations and control structures.

We take modelling as another name for problem solving in teams. For that models have to be compositional, i.e. they have to be carefully formulated with their interfaces to their context - they need to be open and reprogrammable and composable by their users. These possibilities will change the way partners interact in the development of organisations.

Compositionality is the best method we know to cope with complexity. This is also the case for economic models.

And this is especially the case in an economy, where money helps being organised by the division of labour, risk and returns. But it is not the case in thinking about the economy, i.e. the economic modelling process itself is not compositional and organisable by a devision of labour since traditional economic models are not compositional. By that management and operations are desynchronised and the root of many organisational problems.

This was the research question of Viktor with OiCOS ever since the 1990: "How to make economic modelling compositional, programmable and by that organisable by a division of labour?" We have solved a big first step in about 2018 by compositional open games. Since then software tools are under way to also make it applicable in industry. There, since about 2018, when OiCOS became operational, we also had to understand how to communicate what we have discovered to those who may need it like us ourselves in a business context. It took a while for this second step, since if we knew what we were doing it would not be called research. The result is a method to model, compose and programm ourselves with the concepts for economic organisations being now aligned with decidability, computability and programmability. Technically the economics needs a third step of a workflow engine for anticipative agents in quadruple accounting systems of supply and demand chains in national accounting systems. The final step is an integrated modelling environment, IME, for the production of models like in an integrated development environment, IDE, for the production of software.

What is the gain from compositionality and division of labour in engineering? 

Think about the process of designing a car. The engineers of the gearbox, motor and axle deliver their models and in some holo cave (as with tractors at John Deer) the car is virtually assembled and simulated. Then for example a repair team can check whether for repairing the axle the motor needs to be removed - bad - or the drivers can check whether all knobs are reachable during a simulated drive. These interactions of the parts in the whole is mission critical during the design of the car and interaction analysis - aka synthesis or composition - is a major technology to manage the risk of an unsuited model of parts. But synthesis and simulation is also critical for the toolmakers who, in parallel to the design of the car, design and build the toolchain to build the car. Synchronisation of these two processes - building the car and building the tools to build the car - was a major innovation step in the manufacturing industry. Now we aim to synchronise similarly the object and the meta level - operations and management where economic models are at stake.

Now imagine similar power tools for business and economic modelling or management and compare it to the current technology of Power Point slides. How about taking the organigram to the John Deer holo cave? Live - on top of your ERP system's data? You get a glimpse of what this may mean for your products and the organisation of your company, value chain, sector or public administration.

What is the gain from compositionality and division of labour in business, economic and organisational modelling?

Modelling and organisation can be done in specialised teams, preferably by those who are organised, sub-models compose to wholes and are testable and replaceable. Libraries of building blocks of organisations emerge and can be reused. Mass customisation at the level of business and organisational models is possible. Scalability, maintainability, adaptivity and quality of your organisation and product improve. You may simulate different business models and check how they perform in different contexts like macroeconomic scenarios in the areas of your international activities. You can simulate before you actually reorganise - live - on real time data.

The organisation by a division of labour is synchronised with the division of labour when thinking about and modelling the organisation.

Synchronised operational and management processes.

Research Partners

Prof. Dr. Florian Heiss

Florians faculty website

Prof. Dr. Philipp Zahn

  • Mechanism design (good rules for games) and game theory

  • Behavioural economics

  • Experimental design

Philipp's personal website

Dr. Evguenia Sprits

Prof. Neil Ghani, PhD

  • Mathematics

  • Programming language design

  • Open games

Neil's research website

MSP Group (Mathematically Structured Programming)

Prof. Dr. Alexander Kurz

  • Mathematics, coalgebras

  • Programming language design

  • Process logics

Alexander's faculty website

Alexander's research website

Dr. Jules Hedges

  • Open games

  • Mathematics

  • Computer science

Jules' personal web blog

Dr. Renée Menéndez

Renee's personal web blog

Eduard Bretthauer

  • Finance

  • Investors Relations

  • Business Development

Publications

Uncertainty Quantification and Global Sensitivity Analysis for Economic Models

(2019), Quantitative Economics, (preprint), Bruno Sudret, Stefano Marelli, Daniel Harenberg, Viktor Winschel

Compositional Game Theory (2018), Logic in Computer Science, (preprint) Neil Ghani, Jules Hedges, Viktor Winschel, Philipp Zahn

Higher-Order Decision Theory (2017) Algorithmic Decision Theory, (preprint), Evguenia Winschel, Philipp Zahn, Jules Hedges, Paulo Oliva

Selection Equilibria in Higher Order Games (2016), Practical Aspects of Declarative Languages, (preprint),  Philipp Zahn, Jules Hedges, Paulo Oliva, Viktor Winschel, Evguenia Sprit

Coalgebraic Analysis of Subgame-perfect Equilibria in Infinite Games without Discounting (2015), Mathematical Structures in Computer Science, Samson Abramsky, Viktor Winschel

Solving, Estimating and Selecting Nonlinear Dynamic Models without the Curse of Dimensionality (2010) Econometrica, Markus Krätzig, Viktor Winschel

Likelihood Approximation by Numerical Integration on Sparse Grids (2008) Journal of Econometrics, Florian Heiss, Viktor Winschel

Public Deficits and Borrowing Costs: The Missing Half of the Market Discipline

(2001), Journal of Public Finance and Public Choice, Friedrich Heinemann, Viktor Winschel

A Coalgebraic Semantics of Compositional Games in Economics

(2013), arXiV, Achim Blumensath, Viktor Winschel

The Empirical Analysis of Exchange Rate Regimes and Nonlinear Structural Econometrics (2005), PhD Thesis University of Mannheim, Viktor Winschel

Network

Dusko Pavlovic, Brendan Fong, Bob Coecke, Samson Abramsky, David Spivak, Alexander Kurz, Jocelyn Ireson-Paine, John Baez and many more at Oxford, MIT, Strathclyde Glasgow, Amsterdam, Nijmegen, Hawaii and other universities and research institutions.

Conferences

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Academic Network

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