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For many years, political scientists have debated over voter competence in direct democracy. At the core of the discussion is whether this central institution enlightens citizens about political facts. However, scholars have primarily examined if direct democracy fosters general political knowledge even though referendums and ballot initiatives are policy-specific in nature, as citizens vote on particular political proposals. By utilising a range of unique panel survey data collected around four Danish European Union referendums, I show that voters’ knowledge of policy-specific information markedly increased during the campaigns. I also combine the survey data with an original media content analysis and find that the learning of issue-specific facts is more related to the opportunities provided by the media information environment than to individual ability or motivation. These results suggest that a broad group of voters acquire policy-specific facts that help them make informed choices when they are granted full control of political decision-making.
This chapter discusses empirical evidence for a range of decision procedures underlying individuals’ choices in games. These include (1) quantal response equilibria. These are stochastic choice models where a player does not best reply to their beliefs, but instead chooses actions with a higher expected payoff with a higher probability. It also includes (2) cognitive hierarchy models where, roughly speaking, players possess different, discrete levels of cognition. A level-0 player chooses some fixed anchor strategy. And, recursively, having fixed the strategies of players of level 0,...,k-1, a level-k player best responds to a belief that all other players are of a lower level. Lastly, it includes (3) a variety of learning models.
This chapter introduces the three contributions that constitute Part III, “Population Dynamics, Learning, and Biology.” These contributions discuss biology and population dynamics in game theory. The chapters concern models of strategy adaptation: process models. Such models have refined our understanding of Nash and other equilibrium concepts, and the evolution of population shares through time is itself an object of interest from the perspectives of biological reproduction and of learning human (and artificial) agents.
It took some thirty years before the game theoretic ideas of Émile Borel became known to a wider audience, with the publication of the seminal book by John von Neumann and Oskar Morgenstern. Similarly, it took thirty years for the evolutionary approach of Brown, von Neumann, and Nash to be taken up by a wider community. By now, a substantial set of potential updating mechanisms has been modeled and analyzed via game dynamics. Large as it is, it is yet unlikely to capture the full range of adaptive behavior used by human players. A closer relation between the dynamics of nonequilibrium play and empirical data on adaptation and learning is sorely needed. This is a topic where psychology and economics can fruitfully join hands.
This concluding dialogue seeks to convert James’s discursive ideas about education into scenes of lived encounter – between teachers and students, bodies and minds, thinking and feeling – while honoring the possibilities for surprise that such encounters open. In this endeavor, we are also extending Stephanie Hawkins’s work, which reminds us of how James uses the term conversion – meaning “to turn with” or “turn together” – to describe the process through which we come into transformative relation with someone or something other than ourselves. James’s dialectical, often gradual process of “educational” conversion seems to us to offer useful correctives to many incumbent histories of the discipline that would rely on entrenched and reductive genealogies of authority. By reconnecting James’s understanding of conversion with his commitment to conversation, we aim to give living voice to the cluster of deeply felt relations that constitute the life practices we call “teaching” and “learning.”
In this chapter, Jane Thrailkill aligns the instructive aims and literary effects of Jamesian style to underline the broader pedagogical purpose of literary criticism. Her reading of The Principles of Psychology analyzes what she describes as James’s “troping devices,” special literary tools intended to catalyze in his audience a process of “experiential, tactile, sensory education.” In this key early work, Thrailkill argues, James’s stylistic play seeks to “capture the mind in action” – to make the text itself into the kind of experience from which we learn, rather than a static description of that experience. As this essay establishes, James’s experiments in thinking and writing are everywhere motivated by his commitment to pedagogy, combined with his knowledge of how learning actually occurs.
The literature on learning in games interprets equilibrium strategy profiles as the long-run average behavior of agents who are selected at random to play the game. As suggested by Nash, in normal-form games we expect that as the agents accumulate evidence about play of the game they will develop accurate beliefs, so that the stationary points of the process correspond to the Nash equilibria. The definition of Nash equilibrium applies unchanged to games in extensive form, but the learning foundation for it does change, because in games with a nontrivial extensive form simply playing the game repeatedly may not lead agents to know how their opponents would respond to deviations that the agents have not tried. Thus there is no reason to expect learning by myopic agents to lead to Nash equilibrium in general games, as agents may not experiment enough to learn the consequences of deviating from the equilibrium path. Instead, learning is consistent with self-confirming equilibrium, introduced by Fudenberg and Levine in the early 1990s. The focus here is on settings where the agents are patient, so they do have an incentive to experiment. In this case, Nash’s mass action interpretation of equilibrium is again valid. But extensive-form games typically have many equilibria, and not all of them seem equally plausible. An advantage of the learning approach is that some actions that are off-path according to the limiting equilibrium distribution are not counterfactual, but will actually be played by young agents as “experiments,” so that equilibrium refinements can be derived from properties of optimal experimentation.
Abstract: This chapter discusses Dewey’s understanding of change developed in Human Nature and Conduct (1922), with particular focus on the impact of evolutionary theory on his thinking. How does Dewey define the connection between individual development and changes in society? And, centrally, what is the role of education therein? Dewey rejects the categorical division between individual and environment underpinning the two common perspectives that change begins in either the individual or the environment. He proposes a third, interactive view of change. Nardo argues in this chapter that Dewey’s “third view” is grounded significantly in his engagement with evolutionary theory. She shows how Dewey rethinks human behavior, ethics, and morality as evolving in relation to the environment rather than as fixed metaphysical principles. Dewey terms this process “growth.” The chapter shows how creating environments for growth, where the formation of intelligent habits is made possible, is central to the work of the teacher. Nardo discusses how schools can be places where albeit not ideal but at least improved circumstances can be imagined and created to direct individual and societal growth, such that the school, for Dewey, is the realm of (admittedly small) revolutions.
This paper studies the transition to high inflation during the COVID-19 pandemic, using a behavioral version of the New Keynesian model, which replaces the conventional assumption of rational expectations with subjective and heterogeneous expectations. Different shares of agents in the economy form expectations based on alternative views regarding future economic variables: (1) a share of agents forecasts that inflation and output will rapidly revert to steady state; (2) another share forms forecasts based on a model resembling the MSV solution under rational expectations; (3) a third share of agents uses an under-specified model that captures trend-following, adaptive, or extrapolative behavior. Agents learn over time the parameters of their perceived model and they can also shift across different views based on past forecasting performance. The macroeconomic model is estimated using Bayesian methods to fit realized macroeconomic variables and data on expectations from surveys. The results document an additional channel that operates through switches in agents’ perceptions and amplifies the impact of the original inflationary shocks. In response to rising inflation after COVID, agents begin shifting from the mean reversion model to the trend-following specification (with a belief about perceived inflation persistence that is simultaneously revised upward). Consequently, the impact of inflationary shocks is magnified and the effects of monetary policy attenuated.
The chapter begins with discussion of intelligence in simple unicellular organisms followed by that of animals with complex nervous systems. Surprisingly, even organisms that do not have a central brain can navigate their complex environments, forage, and learn. In organisms with central nervous system, neurons and synapses in the brain provide elementary basis of intelligence and memory. Neurons generate action potentials that represent information. Synapses hold memory and control the signal transmission between neurons. A key feature of biological neural circuits is plasticity, that is, their ability to modify the circuit properties based both on stimuli and time intervals between them. This represents one form of learning. The biological brain is not static but continuously evolves based on the experience. The field of AI seeks to learn from biological neural circuitry, emulate aspects of intelligence and learning and attempts to build physical devices and algorithms that can demonstrate features of animal intelligence. Neuromorphic computing therefore requires a paradigm shift in design of semiconductors as well as algorithm foundations that are not necessarily built for perfection, rather for learning.
The CPC presides over a large state-owned economy, which is a key pillar of China’s state capitalist model and a critical source of Party power. The party has adapted its governing strategies of the state-owned sector to maintain its economic dominance without stifling growth and innovation – largely by learning from outside. We highlight the importance of the international system as a source of both policy inputs and pressures to change. We find that in the early phases of China’s marketization process during the 1980s, Chinese policymakers looked to Japan and the World Bank as they restructured state-owned enterprises. In the 1990s, American, European, and Japanese policymakers’ pressure on China to downsize its state sector as a condition of WTO accession was a key consideration in Chinese policymakers’ efforts to build “national champions” capable of competing with foreign multinationals in domestic and international markets. We analyze Chinese leaders’ responses to successive challenges in the state-owned economy, and the resilience of state capitalism which buttresses party rule.
This chapter provides a selection of problems relevant to the field of neuromorphic computing that intersects materials science, electrical engineering, computer science, neural networks, and device design for realizing AI in hardware and algorithms. The emphasis on interdisciplinary nature of neuromorphic computing is apparent.
Artificial intelligence is transforming industries and society, but its high energy demands challenge global sustainability goals. Biological intelligence, in contrast, offers both good performance and exceptional energy efficiency. Neuromorphic computing, a growing field inspired by the structure and function of the brain, aims to create energy-efficient algorithms and hardware by integrating insights from biology, physics, computer science, and electrical engineering. This concise and accessible book delves into the principles, mechanisms, and properties of neuromorphic systems. It opens with a primer on biological intelligence, describing learning mechanisms in both simple and complex organisms, then turns to the application of these principles and mechanisms in the development of artificial synapses and neurons, circuits, and architectures. The text also delves into neuromorphic algorithm design, and the unique challenges faced by algorithmic researchers working in this area. The book concludes with a selection of practice problems, with solutions available to instructors online.
Teaching and learning in college and university classrooms has received increased attention in recent years, including in political science. While historically, political science college education was dominated by the model of lectures and perhaps discussions in brick-and-mortar classrooms, the last two decades have witnessed changes in instructional techniques and considerable variation in pedagogical approaches across instructors and classes. At the same time, we sometimes lack the empirical evidence that these innovative approaches are effective and result in improved learning outcomes. We suggest that sharing our innovative pedagogical approaches becomes even more valuable to the academic community when we add an empirical evaluation of their effectiveness on students’ success and learning.
Since 2003, the European Commission has produced analytical documents (called Impact Assessments, IAs) to appraise its policy proposals. This appraisal process is the cornerstone of the regulatory reform policy of the European Union. Previous research has been concerned with the quality of the IAs in terms of evidence-based policy, usages of economic analysis and other standards of smart regulation. Instead, we move to a different perspective. We draw on the narrative policy framework to explore IAs as a text and discursive instrument. Conceptually, insights from discursive institutionalism are used to explore narratives as tools of coordination within complex organizations such as the European Commission, and as communicative tools through which policy-makers seek to enhance the plausibility, acceptability and, ultimately, legitimacy for their policy proposals. Empirically, we consider a sample of IAs that differ by originating DGs, legal instrument, and level of saliency. The findings show that both in coordinating and communicating policy, the European bureaucracy projects a certain definition of its identity via the narratives it deploys. The Commission may use IAs to produce evidence-based policy, but it also an active narrator. It engages with IAs to provide a presentation of self, to establish EU norms and values, and to create consensus around policy proposals by using causal plots, doomsday scenarios, and narrative dramatization.
As children learn their mother tongues, they make systematic errors. For example, English-speaking children regularly say mouses rather than mice. Because children's errors are not explicitly corrected, it has been argued that children could never learn to make the transition to adult language based on the evidence available to them, and thus that learning even simple aspects of grammar is logically impossible without recourse to innate, language-specific constraints. Here, we examine the role children's expectations play in language learning and present a model of plural noun learning that generates a surprising prediction: at a given point in learning, exposure to regular plurals (e.g. rats) can decrease children's tendency to overregularize irregular plurals (e.g. mouses). Intriguingly, the model predicts that the same exposure should have the opposite effect earlier in learning. Consistent with this, we show that testing memory for items with regular plural labels contributes to a decrease in irregular plural overregularization in six-year-olds, but to an increase in four-year-olds. Our model and results suggest that children's overregularization errors both arise and resolve themselves as a consequence of the distribution of error in the linguistic environment, and that far from presenting a logical puzzle for learning, they are inevitable consequences of it.
In this article, the probability of opening to trade is related to a country's propensity to learn from other countries in its region. It is argued that countries have different motivations to learn, depending upon the responsiveness and accountability of their political regimes. Whereas democracies cannot afford to be dogmatic, authoritarian regimes are less motivated to learn from the experience of others, even if they embrace policies that fail. Using data on trade liberalisation for 57 developing countries in the period 1970–1999, it is found that democracies confronting economic crises are more likely to liberalise trade as a result of learning; among democracies, presidential systems seem to learn more, whereas personalist dictatorial regimes are the most resistant to learning from the experience of others.
The aim of this article is questioning the commonly held assumption that Problem-Based Learning (PBL) is necessarily good for students. Drawing on the experience of teaching a postgraduate module about terrorism and the media as a case study, it shows that PBL can definitely benefit the Political Communication curriculum. However, this is only part of the story. PBL, in fact, appears to have an ‘amplifier’ effect on the learning outcomes of a module: the achievement of outstanding results for those who are committed to the coursework; lower achievement than would have been gained through more ‘traditional’ learning methods for those who do not fully engage with it. As the case study suggests, students’ individual expectations and previous experiences of coursework, although currently overlooked, appear to make all the difference within the learning process.
There is a strong tradition in Britain of volunteering involving a wide range of activities and organisations. Increasingly volunteering is seen as a way of benefiting health and building sustainable communities. In a study in 2007 we aimed to address the research questions: what are the motivations for, barriers to, and benefits of formal practical environmental volunteering for those individuals involved? Qualitative and quantitative data collection was undertaken while spending a day each with ten volunteer groups as they undertook their practical conservation activities. In this paper we focus primarily on the physical, mental and social well-being benefits that volunteers derived from their activities. Our research involved 88 people volunteering regularly in a range of places from scenic natural landscapes to urban green spaces in northern England and southern Scotland. Respondents described a range of benefits they gained from their involvement including improved fitness, keeping alert, meeting others and reducing stress levels. We suggest that practical environmental volunteering has flexibility in the types of activity available and the time scale in which activities are undertaken and therefore can provide a range of physical, social and mental well-being benefits to people with very differing abilities and from different socio-economic backgrounds.
A comparison of speakers’ treatment of two categorically unattested phonotactic structures in Cochabamba Quechua reveals a stronger grammatical prohibition on roots with pairs of ejectives, *[k'ap'u], than on roots with a plain stop followed by an ejective, *[kap'u]. While the distribution of ejectives can be stated as a single restriction on ejectives preceded by stops (ejective or plain), *[-cont, -son][cg], speakers show evidence of having learned an additional constraint that penalizes cooccurring ejectives more harshly, *[cg][cg]. An inductive learning bias in favor of constraints with the algebraic structure of*[cg][cg] is hypothesized (Marcus 2001, Berent et al. 2002, Berent et al. 2012), allowing such constraints to be preferred by learners over constraints like *[-cont, -son][+cg], which penalize sequences of unrelated feature matrices.