Hiroki Sayama

Negotiating Complex Contracts

Work to date on computational models of negotiation has focused almost exclusively on defining contracts consisting of one or a few independent issues and tractable contract spaces. Many real-world contracts, by contrast, are much more complex, consisting of multiple inter-dependent issues and intractably large contract spaces. This paper describes a simulated annealing based approach appropriate for negotiating such complex contracts that achieves near-optimal social welfares for negotiations with binary issue dependencies.

The Dynamics of Collaborative Design: Insights from Complex Systems and Negotiation Research

Almost all complex artifacts nowadays, including physical artifacts such as airplanes, as well as informational artifacts such as software, organizations, business processes, plans, and schedules, are defined via the interaction of many, sometimes thousands of participants, working on different elements of the design. This collaborative design process is typically expensive and time-consuming because strong interdependencies between design decisions make it difficult to converge on a single design that satisfies these dependencies and is acceptable to all participants. Recent research from the complex systems and negotiation literatures has much to offer to the understanding of the dynamics of this process. This paper reviews some of these insights and offers suggestions for improving collaborative design.

A new structurally dissolvable self-reproducing loop evolving in a simple cellular automata space

We constructed a simple evolutionary system, evoloop, on a deterministic nine-state five-neighbor cellular automata (CA) space by improving the structurally dissolvable self-reproducing loop we had previously contrived [14] after Langtons self-reproducing loop [7]. The principal role of this improvement is to enhance the adaptability (a degree of the variety of situations in which structures in the CA space can operate regularly) of the self-reproductive mechanism of loops. The experiment with evoloop met with the intriguing result that, though no mechanism was explicitly provided to promote evolution, the loops varied through direct interaction of their phenotypes, smaller individuals were naturally selected thanks to their quicker self-reproductive ability, and the whole population gradually evolved toward the smallest ones. This result gives a unique example of evolution of self-replicators where genotypical variation is caused by precedent phenotypical variation. Such interrelation of genotype and phenotype would be one of the important factors driving the evolutionary process of primitive life forms that might have actually occurred in ancient times.

Modeling complex systems with adaptive networks

Abstract Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve. Many real-world complex systems can be modeled as adaptive networks, including social networks, transportation networks, neural networks and biological networks. In this paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, non-comprehensive review of recent literature on mathematical/computational modeling and analysis of such networks. We also report our recent work on several applications of computational adaptive network modeling and analysis to real-world problems, including temporal development of search and rescue operational networks, automated rule discovery from empirical network evolution data, and cultural integration in corporate merger.

Hyperinteractive Evolutionary Computation

We propose hyperinteractive evolutionary computation (HIEC), a class of IEC in which the user actively chooses when and how each evolutionary operator is applied. To evaluate the benefits of HIEC, we conducted three human-subject experiments. The first two experiments showed that HIEC is associated with a more positive user experience and produced higher quality designs. The third experiment demonstrates the potential of HIEC as a research tool with which one can record the evolutionary actions taken by human users. Implications, limitations, and future directions of research are discussed.

Negotiating complex contracts

Work to date on computational models of negotiation has focused almost exclusively on defining contracts consisting of one or a few independent issues. Many real-world contracts, by contrast, consist of multiple inter-dependent issues. This paper describes a simulated annealing based approach appropriate for negotiating such complex contracts, evaluates its efficacy, and suggests potentially promising avenues for future work.

Decentralized control and interactive design methods for large-scale heterogeneous self-organizing swarms

We present new methods of decentralized control and interactive design for artificial swarms of a large number of agents that can spontaneously organize and maintain non-trivial heterogeneous formations. Our model assumes no elaborate sensing, computation, or communication capabilities for each agent; the self-organization is achieved solely by simple kinetic interactions among agents. Specifications of the final formations are indirectly and implicitly woven into a list of different kinetic parameter settings and their proportions, which would be hard to obtain with a conventional top-down design method but may be designed heuristically through interactive design processes.

Robust morphogenesis of robotic swarms

Morphogenesis of biological organisms is a programmed yet self-organizing emergent phenomenon that has a lot to offer to engineering designs of man-made robotic systems [1], [2]. Such growing, self-repairing and self-replicating artifacts have been a subject of investigation for long [3], [4], [5], [6], and a number of theoretical models and implementations have been produced in Artificial Life and Bio-Inspired Robotics research communities [7], [8], [9], [10], [11], [12], [13].

Symmetry breaking and coarsening in spatially distributed evolutionary processes including sexual reproduction and disruptive selection.

Sexual reproduction presents significant challenges to formal treatment of evolutionary processes. A starting point for systematic treatments of ecological and evolutionary phenomena has been provided by the gene-centered view of evolution which assigns effective fitness to each allele instead of each organism. The gene-centered view can be formalized as a dynamic mean-field approximation applied to genes in reproduction and selection dynamics. We show that the gene-centered view breaks down for symmetry breaking and pattern formation within a population and show that spatial distributions of organisms with local mating neighborhoods in the presence of disruptive selection give rise to such symmetry breaking and pattern formation in the genetic composition of local populations. Global dynamics follows conventional coarsening of systems with nonconserved order parameters. The results have significant implications for the ecology of genetic diversity and species formation.

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.