Edited by: Carlos Gershenson, Universidad Nacional Autónoma de México, Mexico
Reviewed by: Mahendra Piraveenan, University of Sydney, Australia; Matjaž Perc, University of Maribor, Slovenia
Specialty section: This article was submitted to Computational Intelligence, a section of the journal Frontiers in Robotics and AI
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Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.
This paper originates from a small international workshop on “Methods for Self-Organizing Distributed Systems” that was held in Laubusch, Germany, during October 2015. We name several challenges and give our perspectives for the field of hybrid societies [cf. Eiben (
Typical examples of hybrid societies are investigated in the project ASSISI|
We identify three common, primary challenges in the design of hybrid societies (see Figure
The analysis of hybrid societies using tools of mathematics and computer science is essential to gain deep insights into the dynamics and prominent principles of hybrid systems. Besides allowing for predictions, the formal approach also guides one’s thoughts when designing hybrid societies. The formalization of hybrid societies is the precondition to move from formal specifications to an integrated design process.
From our experience in work with collective hybrid societies, we have the strong belief that our field of research requires a tremendous effort to develop a generic model. Hence, a grand challenge of the design of collective behavior in hybrid societies is to develop an appropriate generic formalization. A truly generic formal model would overcome the diversity of methods and models in the field. If not completely generic, we would at least require a methodology that allows to model a large range of different collective hybrid societies. The purpose of a generic model is to understand the desired system and to gain deep insights. Formalization is necessary to achieve a good understanding of a system’s inner dynamics and, if possible, to predict its outcome. With the optimal model, we could predict future behaviors and effects of hybrid societies. Such a model would permit to analyze a wide variety of collective systems, enable rigorous mathematical comparisons, and help to understand potential problems in system design before realization in simulation, and hardware was achieved.
The formalization approach should be generic and applicable in many domains sharing essential system features. The development of such modeling techniques requires, however, to unify methods, concepts, and definitions from many different fields. It requires a high degree of integration, knowledge about each of these domains, and a high convertibility of the model. First steps toward a unified methodology have been made, for example, in the fields of socio-technical systems (Baxter and Sommerville,
Depending on the system modeled, as well as the type of questions asked, multiple approaches have been developed ranging from purely mathematical equations to spatial multi-agent systems. The total amount of modeling and investigation techniques for homogeneous and heterogeneous collective systems is huge and spans fields such as collective animal behavior, statistical physics, network theory, control theory, opinion dynamics, and diverse subfields of computer science. In order to give a little, incomplete overview, we cite only a few of these, see Table
Physics | Biology/swarm intelligence | Engineering | Computer science | Networks |
---|---|---|---|---|
Spontaneous magnetization, laser theory (Yang, |
Animal groups (Okubo, |
Swarm robotics (Martinoli et al., |
Amorphous computing (Abelson et al., |
Scale-free random networks (Barabási and Albert, |
Percolation, diffusion-limited aggregation (Witten and Sander, |
Swarm intelligence (Bonabeau et al., |
Sensor/actuator networks (Beal and Bachrach, |
World-embedded computation (Payton et al., |
Temporal networks (Holme and Saramäki, |
Self-driven particles (Vicsek et al., |
Opinion dynamics (Schelling, |
Distributed robotics (Weiß, |
Natural computation (Castro, |
Another challenge is the complexity of hybrid societies due to self-organization that contains by definition a multitude of locally interacting agents. Local interactions between agents create dynamic environments, which are complex to model. The agents operate locally but can trigger emergent global patterns; we have different types of agents, and they often live in dynamic environments, which are challenging to model.
For example, a difficulty specific to self-organization is to link the model that describes the global behavior of the system to the model that describes the behavior of the individuals. Defining the so-called micro–macro link is a fundamental issue in both directions (Schelling,
In summary, we have the dynamics of the internal states and local interactions of individual agents on the one side and the overall dynamics of the global system on the other side. The challenge is to find the link between these two sides, which is key to understand and formalize hybrid societies.
The vast number of methods of hybrid societies comes with individual shortcomings. We discuss only a few that may serve as representative examples. The methods of formal specification from the field of software engineering [e.g., see Hoare (
Engineered hybrid societies are complex, and therefore it is difficult to develop
Chemistry and statistical physics provide formal, mechanistic descriptions of hybrid systems. They are the disciplines that inspired, for example within swarm robotics, the most commonly used modeling frameworks (Brambilla et al.,
Less attention has been paid to the formalization of processes leading to self-organization as done in theoretical evolutionary biology and machine learning. In the first case, evolutionary game theory (Nowak,
Even if we assume that we have a formal specification of our hybrid society already, then the actual system design is still a big challenge. We would like to define an integrated process that implements the step from a specification of a self-organizing collective system to the actual real-world system and its deployment in the field. In addition, we have to consider typical requirements for engineered systems, such as safety, reliability, and stability. Also note that we consciously take an engineering perspective on hybrid societies, hence assuming that such self-organizing collective systems can actually be designed. This hypothesis is in line with assumptions made in standard approaches, such as swarm robotics (Martinoli,
Moving from a specification of a hybrid society to a verified implementation on actual hardware remains difficult. Dealing with issues such as time, non-determinism, and scale presents significant challenges to formal methods. Hybrid societies can be designed with a smaller effort for pre-specified environments but for real-world implementations quality characteristics have to be determined (Mahendra Rajah et al.,
The design for reliability and stability needs to be addressed before we are able to deploy many hybrid societies in the real world. The stochasticity and the autonomy present in such systems make assuring reliability a difficult task. Therefore, developing such systems needs to provide evaluation tools that allows for measuring those aspects in a representative way.
Most real-world environments show a high degree of stochasticity, which makes it challenging to deploy hybrid societies in real-world applications. We need methodologies to deal with known uncertainties but also to deal with unforeseen uncertainties. For collective behaviors, we are missing a general model that could be used to verify the system against the expected behaviors. In addition, there might be even unpredictable behaviors [cf., emergent behavior Matarić (
Related to the above complex problems, we also face the challenge of dynamic environments that require non-trivial run-time decisions of our system. Run-time decisions and coupling the collective hybrid society with other systems at run-time require new methodologies. Especially systems with high requirements for robustness operating in dynamic environments have to be able to appropriately self-adapt their behaviors and organization structure (e.g., topology). The required time for non-productive reorganization and adaptation processes should be minimal.
If we allow dynamic changes of the system size, that is, we have an open system, then we need to tackle the challenge of scalability at runtime as well. This adds additional uncertainties introduced by added or removed system components. These changes need to be balanced by the system at run-time to establish a stable and robust system behavior. We often face difficulties when attempting to make guarantees about the behaviors of our systems and in the scenarios when existing techniques can be used they often model a fixed number of agents, making our proofs meaningless as the size of our collective changes dynamically.
Natural collective systems exhibit different features that are remarkable, such as flexibility, adaptability, and robustness. To achieve these through self-organization, they resort to positive and negative feedback mechanisms, the ability to amplify and weaken local individual decisions. The careful design of appropriate feedback processes requires special attention and sophisticated design methods. Besides behavioral feedbacks, collective systems also rely on certain network topologies and network properties, such as power-law degree distributions (scale-free networks), that increase the system’s robustness to the loss of connections (Albert et al.,
Another feature is that of scale-free correlations (Cavagna et al.,
A notable quality of deployed systems is user behavior feeding back steadily into the system. This inevitably entails risks such as collusion, free-riding, or other exploitative and destabilizing actions. The additional challenges, for example in terms of robustness and reliability, therefore need to be considered and firmly rooted in the system design.
Once deployed in the field, bugs are likely to appear in ways unforeseen by the formalization process. This limitation of the formalization task is termed reality gap in robotics and has been studied in recent years. Solutions range from the restriction of the search space (Koos et al.,
In order to allow our system to adapt to changes in its dynamic environment, it requires a sufficient degree of freedom enabling it to self-optimize and to show reliable behavior. We need to allow for methods of self-repair (Ismail and Timmis,
As the reliance on knowledge gained from other scientific disciplines grows, so too does the need for researchers from all fields to be prepared to learn from the insights and techniques of others. The investigated problems are becoming too complex to stay within the scope of a single discipline, and hence, interdisciplinary research is becoming more popular (Helbing et al.,
Engineering has much to offer to the life sciences, but benefits of engagement must be bi-lateral, so that all disciplines benefit from the collaboration. In particular, the contribution of computer science should go beyond that of a mere service to life sciences but instead establish a bidirectional interaction that also scientifically enriches computer science. For example in the context of bio-hybrid societies, modeling and simulation can be an effective vehicle for collaborations between computer scientists (e.g., multi-agent simulations) and biologists (e.g., behavioral models), with computational models being useful to help understand challenges in behavioral biology, yet providing a formal background and inspiration to the creation of an artificial system, for example based on behavioral models of animals (Schmickl and Hamann,
Despite our best will to ensure interdisciplinarity, it remains difficult to achieve in practice. These difficulties stem from the disparity in vocabulary, the different methodologies used, and a general lack of understanding of the way of thinking and the tools available on each side. Time is needed to develop an interdisciplinary collaboration. A common language needs to be developed so that deep and meaningful collaborations are possible.
Once a simple mutual understanding of the available methods and present problems is obtained, it is tempting to merely transfer a method from one field to the other and to directly apply it to a particular problem. However, mastering the complex problems at hand and lastingly improving these systems goes beyond applying existing results but requires true interdisciplinary collaboration. Providing a broad set of insightful tools, only highly integrated research on novel systems leads to a meaningful design method for hybrid societies. Prime examples of successful integration of methods are the integration of robots and fish (Marras and Porfiri,
Despite the success of interdisciplinary research and a lot of hype and lip service in favor of interdisciplinarity, realities still look different. Many institutions and traditions in research are still forming tiny mono-disciplinary worlds. Hence, there is a challenge for individual researchers to fulfill their own discipline’s requirements in terms of measures of success.
A probably obvious solution is to enable the human factor and to form small, strongly linked teams that work interdisciplinarily. In addition, interdisciplinary researchers should receive an elaborate training for the field they are collaborating with. Then the methods that are used to design solutions for different problems should transgress disciplinary bounds, in order to allow re-use of methods across fields of research.
Similarly to the situation when travelers have to adapt to local customs, all involved parties need to compromise. The common vocabulary needs to be found and the various perspectives and the different knowledge need to be understood. Only then one can start to discover where and how both sides can benefit from each other or how they can join forces to design novel methods for hybrid societies.
We have identified three primary challenges of designing hybrid societies: formalization, system design, and interdisciplinarity. All of them require a lot of attention and a major effort to be overcome. However, a generic formalization approach and efficient interdisciplinary collaborations shall create synergies and enable us to re-use methods at intersections between disciplines. An appropriate system design approach would enable us to quickly deploy safe, reliable, and stable systems in hardware.
HH and YK wrote the paper and organized the overall writing process. All other authors contributed about equally to the writing process.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MP declared a shared affiliation, though no other collaboration, with one of the authors (AZ) to the handling Editor, who ensured that the process nevertheless met the standards of a fair and objective review.
This work was partially supported by the European Union’s Horizon 2020 research and innovation program under the FET grant agreement “