Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization

Evolution of self-organized division of labor in a response threshold model

Division of labor in social insects is determinant to their ecological success. Recent models emphasize that division of labor is an emergent property of the interactions among nestmates obeying to simple behavioral rules. However, the role of evolution in shaping these rules has been largely neglected. Here, we investigate a model that integrates the perspectives of self-organization and evolution. Our point of departure is the response threshold model, where we allow thresholds to evolve. We ask whether the thresholds will evolve to a state where division of labor emerges in a form that fits the needs of the colony. We find that division of labor can indeed evolve through the evolutionary branching of thresholds, leading to workers that differ in their tendency to take on a given task. However, the conditions under which division of labor evolves depend on the strength of selection on the two fitness components considered: amount of work performed and on worker distribution over tasks. When selection is strongest on the amount of work performed, division of labor evolves if switching tasks is costly. When selection is strongest on worker distribution, division of labor is less likely to evolve. Furthermore, we show that a biased distribution (like 3:1) of workers over tasks is not easily achievable by a threshold mechanism, even under strong selection. Contrary to expectation, multiple matings of colony foundresses impede the evolution of specialization. Overall, our model sheds light on the importance of considering the interaction between specific mechanisms and ecological requirements to better understand the evolutionary scenarios that lead to division of labor in complex systems

Internationalisation of Innovation: Why Chip Design Moving to Asia

This paper will appear in International Journal of Innovation Management, special issue in honor of Keith Pavitt, (Peter Augsdoerfer, Jonathan Sapsed, and James Utterback, guest editors), forthcoming. Among Keith Pavitt's many contributions to the study of innovation is the proposition that physical proximity is advantageous for innovative activities that involve highly complex technological knowledge But chip design, a process that creates the greatest value in the electronics industry and that requires highly complex knowledge, is experiencing a massive dispersion to leading Asian electronics exporting countries. To explain why chip design is moving to Asia, the paper draws on interviews with 60 companies and 15 research institutions that are doing leading-edge chip design in Asia. I demonstrate that "pull" and "policy" factors explain what attracts design to particular locations. But to get to the root causes that shift the balance in favor of geographical decentralization, I examine "push" factors, i.e. changes in design methodology ("system-on-chip design") and organization ("vertical specialization" within global design networks). The resultant increase in knowledge mobility explains why chip design - that, in Pavitt's framework is not supposed to move - is moving from the traditional centers to a few new specialized design clusters in Asia. A completely revised and updated version has been published as: " Complexity and Internationalisation of Innovation: Why is Chip Design Moving to Asia?," in International Journal of Innovation Management, special issue in honour of Keith Pavitt, Vol. 9,1: 47-73.

Cooperation, collective action, and the archeology of large-scale societies

Archeologists investigating the emergence of large-scale societies in the past have renewed interest in examining the dynamics of cooperation as a means of understanding societal change and organizational variability within human groups over time. Unlike earlier approaches to these issues, which used models designated voluntaristic or managerial, contemporary research articulates more explicitly with frameworks for cooperation and collective action used in other fields, thereby facilitating empirical testing through better definition of the costs, benefits, and social mechanisms associated with success or failure in coordinated group action. Current scholarship is nevertheless bifurcated along lines of epistemology and scale, which is understandable but problematic for forging a broader, more transdisciplinary field of cooperation studies. Here, we point to some areas of potential overlap by reviewing archeological research that places the dynamics of social cooperation and competition in the foreground of the emergence of large-scale societies, which we define as those having larger populations, greater concentrations of political power, and higher degrees of social inequality. We focus on key issues involving the communal-resource management of subsistence and other economic goods, as well as the revenue flows that undergird political institutions. Drawing on archeological cases from across the globe, with greater detail from our area of expertise in Mesoamerica, we offer suggestions for strengthening analytical methods and generating more transdisciplinary research programs that address human societies across scalar and temporal spectra

Talking about routines in the field: the emergence of organizational capabilities in a new cellular phone network company

No abstract availabl

Lenia and Expanded Universe

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc

Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult?

Embodied evolutionary robotics is an on-line distributed learning method used in collective robotics where robots are facing open environments. This paper focuses on learning behavioral specialization, as defined by robots being able to demonstrate different kind of behaviors at the same time (e.g., division of labor). Using a foraging task with two resources available in limited quantities, we show that behavioral specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important – though not always mandatory – roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labor in the general case, i.e., where it is not possible to guess before deployment if behavioral specialization is required or not, and gives directions to overcome current limitations.This work is supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 640891, and the ERC Advanced Grant EPNet (340828). Part of the experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER, and several Universities as well as other funding bodies (see https://www.grid5000.fr). The other parts of the simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center – Centro Nacional de Supercomputacion (The Spanish National Supercomputing Center).Peer ReviewedPostprint (published version