7,984 research outputs found
Neuroeconomics: How Neuroscience Can Inform Economics
Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified ß-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information
The Online Laboratory: Conducting Experiments in a Real Labor Market
Online labor markets have great potential as platforms for conducting
experiments, as they provide immediate access to a large and diverse subject
pool and allow researchers to conduct randomized controlled trials. We argue
that online experiments can be just as valid---both internally and
externally---as laboratory and field experiments, while requiring far less
money and time to design and to conduct. In this paper, we first describe the
benefits of conducting experiments in online labor markets; we then use one
such market to replicate three classic experiments and confirm their results.
We confirm that subjects (1) reverse decisions in response to how a
decision-problem is framed, (2) have pro-social preferences (value payoffs to
others positively), and (3) respond to priming by altering their choices. We
also conduct a labor supply field experiment in which we confirm that workers
have upward sloping labor supply curves. In addition to reporting these
results, we discuss the unique threats to validity in an online setting and
propose methods for coping with these threats. We also discuss the external
validity of results from online domains and explain why online results can have
external validity equal to or even better than that of traditional methods,
depending on the research question. We conclude with our views on the potential
role that online experiments can play within the social sciences, and then
recommend software development priorities and best practices
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
Complexity Theory, Adaptation, and Administrative Law
Recently, commentators have applied insights from complexity theory to legal analysis generally and to administrative law in particular. This Article focuses on one of the central problems that complexity. theory addresses, the importance and mechanisms of adaptation within complex systems. In Part I, the Article uses three features of complex adaptive systems-emergence from self-assembly, nonlinearity, and sensitivity to initial conditions-and explores the extent to which they may add value as a matter of positive analysis to the understanding of change within legal systems. In Part H, the Article focuses on three normative claims in public law scholarship that depend explicitly or implicitly on notions of adaptation: that states offer advantages over the federal government because experimentation can make them more adaptive, that federal agencies should themselves become more experimentalist using the tool of adaptive management, and that administrative agencies shou Id adopt collaborative mechanisms in policymaking. Using two analytic tools found in the complexity literature, the genetic algorithm and evolutionary game theory, the Article tests the extent to which these three normative claims are borne out
SAPS and Digital Games: Improving Mathematics Transfer and Attitudes in Schools
Many suggest that digital games are a way to address problems with schools, yet research on their ability to promote problem solving, critical thinking, and twenty-first century skill sets appears to be mixed. In this chapter, I suggest that the problem lies not with digital games, but with our conceptualization of what it means to promote problem solving and critical thinking, and how transfer of such skills works in general and, specifically, with games. The power of digital games lies not in some magical power of the medium, but from embedded theories (e.g., situated learning and problem-centered instruction) and from good instructional design (the principles of learning and teaching to which all good instruction must adhere). This chapter describes situated, authentic problem solving (SAPS): a model to explain how digital games can promote transfer and improve attitudes toward mathematics. By examining research on the instructional practices (situated learning) and outcomes (transfer, problem solving, attitudes) that lie at the heart of SAPS, we can chart a path forward for best practices of digital games in mathematics education
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