Simple Efficient Contracts in Complex Environments

The paper studies a general model of hold-up in a setting encompassing the models of Segal (1999) and Che and Hausch (1999) among others. It is shown that if renegotiation is modelled as an infinite-horizon non-cooperative bargaining game then, with a simple initial contract, an efficient equilibrium will generally exist. The contract gives authority to one party to set the terms of trade and gives the other party a non-expiring option to trade at these terms. The difference from standard results arises because the existing contract ensures that the renegotiation game has multiple equilibria; the multiplicity of continuation equilibria can be used to enforce efficient investment

Model of human collective decision-making in complex environments

A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, consisting in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by two different forces: (i) the self-interest, which pushes them to increase their own fitness values, and (ii) the social interactions, which push individuals to reduce the diversity of their opinions in order to reach consensus. Results show that the performance of the group is strongly affected by the strength of social interactions and by the level of knowledge of the individuals. Increasing the strength of social interactions improves the performance of the team. However, too strong social interactions slow down the search of the optimal solution and worsen the performance of the group. In particular, we find that the threshold value of the social interaction strength, which leads to the emergence of a superior intelligence of the group, is just the critical threshold at which the consensus among the members sets in. We also prove that a moderate level of knowledge is already enough to guarantee high performance of the group in making decisions.Comment: 12 pages, 8 figues in European Physical Journal B, 201

Design Within Complex Environments: Collaborative Engineering in the Aerospace Industry

The design and the industrialization of an aircraft, a major component, or an aerostructure is a complex process. An aircraft like the Airbus A400M is composed of about 700,000 parts (excluding standard parts). The parts are assembled into aerostructures and major components, which are designed and manufactured in several countries all over the world. The introduction of new Product Lifecycle Management (PLM) methodologies, procedures and tools, and the need to reduce time-to-market, led Airbus Military to pursue new working methods to deal with complexity. Collaborative Engineering promotes teamwork to develop product, processes and resources from the conceptual phase to the start of the serial production. This paper introduces the main concepts of Collaborative Engineering as a new methodology, procedures and tools to design and develop an aircraft, as Airbus Military is implementing. To make a Proof of Concept (PoC), a pilot project, CALIPSOneo, was launched to support the functional and industrial design process of a medium size aerostructure. The aim is to implement the industrial Digital Mock-Up (iDMU) concept and its exploitation to create shop fl oor documentation

Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings

Acoustic Identification of Flat Spots On Wheels Using Different Machine Learning Techniques

BMBF, 01IS18049B, ALICE III - Autonomes Lernen in komplexen Umgebungen 3 (Autonomous Learning in Complex Environments 3