An Investigation of the Free Energy Principle for Emotion Recognition

This paper offers a prospectus of what might be achievable in the development of emotional recognition devices. It provides a conceptual overview of the free energy principle; including Markov blankets, active inference, and-in particular-a discussion of selfhood and theory of mind, followed by a brief explanation of how these concepts can explain both neural and cultural models of emotional inference. The underlying hypothesis is that emotion recognition and inference devices will evolve from state-of-the-art deep learning models into active inference schemes that go beyond marketing applications and become adjunct to psychiatric practice. Specifically, this paper proposes that a second wave of emotion recognition devices will be equipped with an emotional lexicon (or the ability to epistemically search for one), allowing the device to resolve uncertainty about emotional states by actively eliciting responses from the user and learning from these responses. Following this, a third wave of emotional devices will converge upon the user's generative model, resulting in the machine and human engaging in a reciprocal, prosocial emotional interaction, i.e., sharing a generative model of emotional states

An Empirical Analysis of the Output Declines in Three Eastern European Countries

The declines in economic activity experienced by Bulgaria, the Czech and Slovak Federal Republic, and Romania in the period since market-oriented reforms were initiated are analyzed. After reviewing developments in these three countries, the paper empirically investigates two questions that are central to an interpretation of the output decline. First, to what extent does the output fall reflect "structural change," or a reallocation of resources across sectors, rather than a conventional macroeconomic recession? Second, to what extent have demand-side or supply-side forces been dominant in generating the output decline?

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today's news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent's beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., 'tweets') while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network's perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.publishe

Inflation and stabilization in transition economies: An analytical interpretation of the evidence

A simple model is developed to understand inflationary pressures and stabilization in non-market economies. It is shown that in the typical planned economy, the endogeneity of the money supply and the over-determination of the system (given that both prices and wages are set by the planners) imply that a permanent increase in wages leads to an ever-increasing monetary overhang. The model also suggests that price liberalization should lead to a price level overshooting provided that wages remain a nominal anchor. In light of the model, the paper reviews the inflation and stabilization experiences of several transition economies in Eastern Europe and the former Soviet Union. The paper concludes that (i) transition economies have suffered from essentially the same inflationary pressures as did planned economies, and (ii) the exchange rate has been more effective than money as a nominal anchor in reducing inflationinflation, stabilization, transition economies,