Opinion amplification causes extreme polarization in social networks.

Extreme polarization of opinions fuels many of the problems facing our societies today, from issues on human rights to the environment. Social media provides the vehicle for these opinions and enables the spread of ideas faster than ever before. Previous computational models have suggested that significant external events can induce extreme polarization. We introduce the Social Opinion Amplification Model (SOAM) to investigate an alternative hypothesis: that opinion amplification can result in extreme polarization. SOAM models effects such as sensationalism, hype, or "fake news" as people express amplified versions of their actual opinions, motivated by the desire to gain a greater following. We show for the first time that this simple idea results in extreme polarization, especially when the degree of amplification is small. We further show that such extreme polarization can be prevented by two methods: preventing individuals from amplifying more than five times, or through consistent dissemination of balanced opinions to the population. It is natural to try and have the loudest voice in a crowd when we seek attention; this work suggests that instead of shouting to be heard and generating an uproar, it is better for all if we speak with moderation.

From evolutionary ecosystem simulations to computational models of human behavior.

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence.

The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities.

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Evaluating clustering methods within the Artificial Ecosystem Algorithm and their application to bike redistribution in London.

This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to solve the problem exactly. This motivates our use of the heuristic Artificial Ecosystem Algorithm (AEA) to find good solutions in a reasonable amount of time. The AEA is designed to take advantage of highly distributed computer architectures and adapt to changing problems. In the AEA a problem is first decomposed into its relative sub-components; they then evolve solution building blocks that fit together to form a single optimal solution. Three variants of the AEA centred on evaluating clustering methods are presented: the baseline AEA, the community-based AEA which groups stations according to journey flows, and the Adaptive AEA which actively modifies clusters to cater for changes in demand. We applied these AEA variants to the redistribution problem prominent in bike share schemes (BSS). The AEA variants are empirically evaluated using historical data from Santander Cycles to validate the proposed approach and prove its potential effectiveness.

Pain expressiveness and altruistic behavior: an exploration using agent-based modeling.

Predictions which invoke evolutionary mechanisms are hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interactions in specific physical or social environments over many generations. The outcomes have implications for understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/nonexpresser strategies) when injured and of helping, ignoring, or exploiting another in pain (altruistic/nonaltruistic/selfish strategies). Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury interrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, contingencies that evident from experimental work with a variety of mammals, over a few interactions, were replicated in the agent-based model after selection pressure over many generations. More energy-demanding expression of pain reduced its frequency in successive generations, and increasing injury frequency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased expression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits of helping hardly changed its frequency, whereas increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent-based modeling allows simulation of complex behaviors and environmental pressures over evolutionary time.

iStethoscope: a demonstration of the use of mobile devices for auscultation.

iStethoscope Pro is the first piece of software (an "App") produced for iOS devices, which enabled users to exploit their smartphones, music players, or tablets as stethoscopes. The software exploits the built-in microphone (and supports externally added microphones) and performs real-time amplification and filtering to enable heart sounds to be heard with high fidelity. The software also enables the heart sounds to be recorded, analyzed using a spectrogram, and to be transmitted to others via e-mail. This chapter describes the motivation, functionality, and results from this work.

Staging the self-assembly process: inspiration from biological development.

One of the practical challenges facing the creation of self-assembling systems is being able to exploit a limited set of fixed components and their bonding mechanisms. The method of staging divides the self-assembly process into time intervals, during which components can be added to, or removed from, an environment at each interval. Staging addresses the challenge of using components that lack plasticity by encoding the construction of a target structure in the staging algorithm itself and not exclusively in the design of the components. Previous staging strategies do not consider the interplay between component physical features (morphological information). In this work we use morphological information to stage the self-assembly process, during which components can only be added to their environment at each time interval, to demonstrate our concept. Four experiments are presented, which use heterogeneous, passive, mechanical components that are fabricated using 3D printing. Two orbital shaking environments are used to provide energy to the components and to investigate the role of morphological information with component movement in either two or three spatial dimensions. The benefit of our staging strategy is shown by reducing assembly errors and exploiting bonding mechanisms with rotational properties. As well, a doglike target structure is used to demonstrate in theory how component information used at an earlier time interval can be reused at a later time interval, inspired by the use of a body plan in biological development. We propose that a staged body plan is one method toward scaling self-assembling systems with many interacting components. The experiments and body plan example demonstrate, as proof of concept, that staging enables the self-assembly of more complex morphologies not otherwise possible.

Methods for improving simulations of biological systems: systemic computation and fractal proteins.

Modelling and simulation are becoming essential for new fields such as synthetic biology. Perhaps the most important aspect of modelling is to follow a clear design methodology that will help to highlight unwanted deficiencies. The use of tools designed to aid the modelling process can be of benefit in many situations. In this paper, the modelling approach called systemic computation (SC) is introduced. SC is an interaction-based language, which enables individual-based expression and modelling of biological systems, and the interactions between them. SC permits a precise description of a hypothetical mechanism to be written using an intuitive graph-based or a calculus-based notation. The same description can then be directly run as a simulation, merging the hypothetical mechanism and the simulation into the same entity. However, even when using well-designed modelling tools to produce good models, the best model is not always the most accurate one. Frequently, computational constraints or lack of data make it infeasible to model an aspect of biology. Simplification may provide one way forward, but with inevitable consequences of decreased accuracy. Instead of attempting to replace an element with a simpler approximation, it is sometimes possible to substitute the element with a different but functionally similar component. In the second part of this paper, this modelling approach is described and its advantages are summarized using an exemplar: the fractal protein model. Finally, the paper ends with a discussion of good biological modelling practice by presenting lessons learned from the use of SC and the fractal protein model.

Nature's Batik: a computer evolution model of diatom valve morphogenesis.

This paper describes a novel computer simulation that uses evolution to design functioning raphid pennate diatom valves. The model of valve morphogenesis used is based on current theories that highlight the importance of cytoskeletal elements in valve development. An "organic" negative imprint is grown in a grid-based system, using both local and global rules to dictate grid cell states. Silica then diffuses out into all remaining grid cells. This model is shown to generate raphid pennate diatom valves capable of functioning as cell walls. At every stage of development the generated valves are consistent with observations of real diatom valve growth. This model of diatom valve morphogenesis is interestingly similar to the negative technique used by artists in batik painting.

Evolving beyond perfection: an investigation of the effects of long-term evolution on fractal gene regulatory networks.

This paper continues a theme of exploring algorithms based on principles of biological development for tasks such as pattern generation, machine learning and robot control. Previous work has investigated the use of genes expressed as fractal proteins to enable greater evolvability of gene regulatory networks (GRNs). Here, the evolution of such GRNs is investigated further to determine whether evolution exhibits natural tendencies towards efficiency and graceful degradation of developmental programs. Experiments where "perfect" GRNs are evolved for a further thousand generations without the addition of any further selection pressure, confirm this hypothesis. After further evolution, the perfect GRNs operate in a more efficient manner (using fewer proteins) and show an improved ability to function correctly with missing genes. When the algorithm is applied to applications (e.g. robot control) this equates to efficient and fault-tolerant controllers.

Telephone triage in Western Australia.

We describe the development and operations of the first large-scale Australian medical telephone triage centre. Studies have commenced to evaluate efficacy and safety of the service, as well as gauge the impact on demand for healthcare services.