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

Reaching the Unreachable: A Method for Early Stage Software Startups to Reach Inaccessible Stakeholders within Large Corporations

Bridging the gap in software development from idea to need remains a difficult task for startups, especially when users are unreachable within the black boxes of large corporations. The lack of customer collaboration results in the failure of requirements elicitation and subsequently the failure of software products. In this work, we describe a simple but effective method to enable startups to reach relevant hidden stakeholders within large corporations and elicit requirements from them. AdvisorNet leverages professional networks and social media to find senior domain experts. These carefully selected advisors bridge the gap from the outside world to the hidden corporate structure and social networks in the target corporation and enable highly successful elicitation from stakeholders. We demonstrate AdvisorNet with a real-world case study in which a London-based startup successfully connected with multiple advisors who then introduced previously unreachable users for requirements elicitation and decision-makers to achieve their goal of trialing their product within the large corporations. Finally, we provide suggestions for future research to formalize the method further and enable it to become rigorous and repeatable

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

Teams Frightened of Failure Fail More: Modelling Reward Sensitivity in Teamwork

According to Gray's Reinforcement Sensitivity Theory (RST), individuals have differing sensitivities to rewards and punishments, which in turn affect their behaviours. The behavioural inhibition system (BIS) is associated with sensitivity to punishment while the behavioural activation system (BAS) is associated with sensitivity to reward. In this work, we model BIS/BAS by supplementing an existing agent-based model of team collaboration in order to explore the combined effect on team performance for a more complex and realistic personality structure. We investigate the significance of BIS/BAS on team behaviour for tasks with differing levels of uncertainty. Findings include a prediction that for tasks with uncertainty, a majority of personality types are significantly influenced by behavioural activation system, and that all personality types are significantly negatively influenced by behavioural inhibition system. The more sensitive to punishments, the worse teams perform

Constraint Handling in Genotype to Phenotype Mapping and Genetic Operators for Project Staffing

Project staffing in many organisations involves the assignment of people to multiple projects while satisfying multiple constraints. The use of a genetic algorithm with constraint handling performed during a genotype to phenotype mapping process provides a new approach. Experiments show promise for this technique

Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders

Nature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm. Fig. 1.Search space Optimization with Latent Variable Evolution (SOLVE). An optimizer produces a dataset of random solutions satisfying an extra criterion (e.g., constraint or secondary objective). A variational autoencoder learns this dataset and produces a learned latent representation biased towards the desired region of the search space. This learned representation is then used by a genetic algorithm to find solutions that meet the objective and extra criterion together

Generating synthetic energy usage data to enable machine learning for sustainable accommodation

Machine Learning has the potential to discover new correlations between energy usage in apartments and variables such as seasonality, apartment location, size, efficiency and details of those staying in the apartments, thus helping apartments to become more sustainable and helping those who stay in them to use less energy. The biggest impedance to creating such ML tools is lack of viable data - without the data, the tools cannot be created - yet it is not feasible to wait for several years' worth of good data before creating the tools. Here we present a solution to this problem: the use of a digital twin to generate synthetic data. This approach is viable even when there is no existing data, but when expert knowledge about the relationship between systems exist. To achieve this, we develop a new agent-based synthetic data generator (ASDG) and explore a case study with a corporate housing and luxury alternate accommodation marketplace called TheSqua.re. We show that unlimited quantities of realistic data can be automatically generated, including data for different scenarios, and that it can be used by Machine Learning to discover the underlying correlations

Using PseudoGravity to Attract People: An Automated Approach to Engaging a Target Audience using Twitter

We introduce the PseudoGravity tool, an automated social media system that establishes a social media presence in the area of interest of a target audience, identifies target users that are open to connect, engages with them, and elicits a complex response and time investment from them. In this work, we use Twitter as the social media platform and an extensive survey as the activity requiring time investment. We evaluate the tool by using it to find and survey a challenging target – science fiction authors – and compare its results with other methods of automated online surveys. In 28 months, the Twitter account managed by the tool achieved more than 12,000 followers, and achieved monthly Tweet Impressions of more than 250,000. The tool also achieved a high survey response rate of 71% and a completion rate of 83% compared to 30% and 47% achieved by typical online surveys, and high numbers of words and characters entered for questions that required free text input. In addition, this work successfully surveyed more than 500 science fiction writers and gained new understandings of the challenges that e-publishing is bringing to their profession

Fault Tolerant Fusion of Office Sensor Data using Cartesian Genetic Programming

The Smart Grid of the future will enable a cleaner, more efficient and fault tolerant system of power distribution. Sensing power use and predicting demand is an important component in the Smart Grid. In this work, we describe a Cartesian Genetic Programming (CGP) system applied to a smart office. In the building, power usage is directly proportional to the number of people present. CGP is used to perform data fusion on the data collected from smart sensors embedded in the building in order to predict the number of people over a two-month period. This is a challenging task, as the sensors are unreliable, resulting in incomplete data. It is also challenging because in addition to normal staff, the building underwent renovation during the test period, resulting the presence of additional personnel who would not normally be present. Despite these difficult real-world issues, CGP was able to learn human-readable rules that when used in combination, provide a method for data fusion that is tolerant to the observed faults in the sensors

All in Good Team: Optimising Team Personalities for Different Dynamic Problems and Task Types

Change is inevitable in this fast-moving world. As the environment and people’s needs continuously change, so must the project. In our previous work, we developed an agent-based model of human collaboration that incorporates individual personalities. In this work, we applied a genetic algorithm to select the optimal personality combinations of a team in order to cope with different types of project change. We studied change in the context of three types of tasks: disjunctive (team performance is the performance achieved by the best performing individual), conjunctive (team performance is the performance achieved by the worst performing individual), and additive (team performance is the total performance of the group). Results reveal that different compositions of team personalities are suitable for different dynamic problems and task types. In particular, optimal personalities found for static problems differ from optimal personalities found for dynamic problems