‘Ethical Novelty’: new insights into economic change

Agents’ knowledge combines their perception of what reality is with their conception of what reality should be. “Ethical dynamics” refers to the evolution in the latter conception. This is a key element to explain changes in agents’ objectives of action, which usually do not result simply from interaction or “cognitive dynamics”. “Ethical novelties” are important sources of economic change. They consist of changes in the structure of action objectives which result from ethical dynamics.knowledge; action plan; ethical novelty; cognitive and ethical dynamics; economic change

When overconfident agents slow down collective learning

This paper presents a model of influence where agents' beliefs are based on an objective reality, such as the properties of an environment. The perception of the objective reality is not direct: all agents know is that the more correct a belief, the more successful the actions that are deduced from this belief. (A pair of agents can influence each other when )Agents can influence eachother by pair when they perform a joint action. They are not only defined by individual beliefs, but also idyosynchratic confidence in their belief - this means that they are not all willing to (engage in action with) act with agents with a different belief and to be influenced by them. We show here that the distribution of confidence in the group has a huge impact on the speed and quality of collective learning and in particular that a small number of overconfident agents can prevent the whole group frow learning properly.agent-based computational economics;belief dissemination;bounded-confidence;simulation agents;social influence

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar

Simulation of population’s reproductive behaviour patterns within an agent-oriented regional model

The study focuses on the research on how the unevenness of demographic transition affects the social and demographic characteristics and their dynamics of a region’s population. The research was conducted by means of computerized experiments (simulations) set within an original agent-oriented model. The study features the structure of the model represented by an artificial society, with its members (agents) being attributed their personal characteristics in such a way that they would imitate the gender and age of the region’s population. The agents are divided into two groups which differ in their reproductive strategy. Agents from Group 1 adhere to the traditional strategy characterized by a high birth rate, while the agents from Group 2 follow the modern strategy resulting in a markedly low birth rate. With the application of probabilistic mechanisms, the natural birth-death processes are imitated within the model. The extinction of agents occurs in accordance with the death rates adjusted for age and gender but remaining the same for the whole population. In the model, the appearance of new agents (birth of children) results from the choice made by reproductive-aged female agents, and their choice is influenced by the subjective traits determined by their group. The age and social structure of the regional population are generally formed as a result of the aggregation of particular agents’ activity. The model has been applied in a range of experiments on forecasting the number and structure of the population in an assumed region. The results showed that despite the apparent simplification of the reality, the developed agent-oriented model correctly represents both the initial condition of the regional population including the gender, age and social structure and the dynamics of the population’s basic characteristics.The research has been supported by the Russian Science Foundation (Project № 14-18-01968)

‘Ethical Novelty’: new insights into economic change

Agents’ knowledge combines their perception of what reality is with their conception of what reality should be. “Ethical dynamics” refers to the evolution in the latter conception. This is a key element to explain changes in agents’ objectives of action, which usually do not result simply from interaction or “cognitive dynamics”. “Ethical novelties” are important sources of economic change. They consist of changes in the structure of action objectives which result from ethical dynamics

Theorizing ideas and discourse in political science: intersubjectivity, neo-institutionalisms, and the power of ideas

Oscar Larsson’s (2015) essay condemns discursive institutionalism for the “sin” of subjectivism. In reality, however, discursive institutionalism emphasizes the intersubjective nature of ideas through its theorization of agents’ “background ideational abilities” and “foreground discursive abilities.” It also avoids relativism by means of Wittgenstein’s distinction between experiences of everyday life and pictures of the world. Contrary to Larsson, what truly separates post-structuralism from discursive institutionalism is the respective approaches’ theorization of the relationship of power to ideas, with discursive institutionalists mainly focused on persuasive power through ideas, while post-structuralists focus on the structural power in ideas or on coercive power over ideas