51,618 research outputs found
An Evolutionary Learning Approach for Adaptive Negotiation Agents
Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications
Reconfiguring Household Management in Times of Discontinuity as an Open System: The Case of Agro-food Chains
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article is based upon a heterodox approach to economics that rejects the
oversimplification made by closed economic models and the mainstream concept
of âexternality.â This approach re-imagines economics as a holistic evaluation of
resources versus human needs, which requires judgement based on understanding
of the complexity generated by the dynamic relations between different systems.
One re-imagining of the economic model is as a holistic and systemic evaluation of
agri-food systemsâ sustainability that was performed through the multi-dimensional
Governance Assessment Matrix Exercise (GAME). This is based on the five capitals
model of sustainability, and the translation of qualitative evaluations into quantitative
scores. This is based on the triangulation of big data from a variety of sources. To
represent quantitative interactions, this article proposes a provisional translation of
GAMEâs qualitative evaluation into a quantitative form through the identification of
measurement units that can reflect the different capital dimensions. For instance, a
post-normal, ecological accounting method, Emergy is proposed to evaluate the natural
capital. The revised GAME re-imagines economics not as the âdismal science,â but
as one that has potential leverage for positive, adaptive and sustainable ecosystemic
analyses and global âhouseholdâ management. This article proposes an explicit
recognition of economics nested within the social spheres of human and social capital
which are in turn nested within the ecological capital upon which all life rests and is
truly the bottom line. In this article, the authors make reference to an on-line retailer of
local food and drink to illustrate the methods for evaluation of the five capitals model
Foraging as an evidence accumulation process
A canonical foraging task is the patch-leaving problem, in which a forager
must decide to leave a current resource in search for another. Theoretical work
has derived optimal strategies for when to leave a patch, and experiments have
tested for conditions where animals do or do not follow an optimal strategy.
Nevertheless, models of patch-leaving decisions do not consider the imperfect
and noisy sampling process through which an animal gathers information, and how
this process is constrained by neurobiological mechanisms. In this theoretical
study, we formulate an evidence accumulation model of patch-leaving decisions
where the animal averages over noisy measurements to estimate the state of the
current patch and the overall environment. Evidence accumulation models belong
to the class of drift diffusion processes and have been used to model decision
making in different contexts. We solve the model for conditions where foraging
decisions are optimal and equivalent to the marginal value theorem, and perform
simulations to analyze deviations from optimal when these conditions are not
met. By adjusting the drift rate and decision threshold, the model can
represent different strategies, for example an increment-decrement or counting
strategy. These strategies yield identical decisions in the limiting case but
differ in how patch residence times adapt when the foraging environment is
uncertain. To account for sub-optimal decisions, we introduce an
energy-dependent utility function that predicts longer than optimal patch
residence times when food is plentiful. Our model provides a quantitative
connection between ecological models of foraging behavior and evidence
accumulation models of decision making. Moreover, it provides a theoretical
framework for potential experiments which seek to identify neural circuits
underlying patch leaving decisions
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Occupational Cultures as a Challenge to Technological Innovation
This paper explains conflict over technological process innovation in cultural terms, drawing primarily on a case study of electric power distribution and strategies to automate its operation
Games for a new climate: experiencing the complexity of future risks
This repository item contains a single issue of the Pardee Center Task Force Reports, a publication series that began publishing in 2009 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future.This report is a product of the Pardee Center Task Force on Games for a New Climate, which met at Pardee House at Boston University in March 2012. The 12-member Task Force was convened on behalf of the Pardee Center by Visiting Research Fellow Pablo Suarez in collaboration with the Red Cross/Red Crescent Climate Centre to âexplore the potential of participatory, game-based processes for accelerating learning, fostering dialogue, and promoting action through real-world decisions affecting the longer-range future, with an emphasis on humanitarian and development work, particularly involving climate risk management.â
Compiled and edited by Janot Mendler de Suarez, Pablo Suarez and Carina Bachofen, the report includes contributions from all of the Task Force members and provides a detailed exploration of the current and potential ways in which games can be used to help a variety of stakeholders â including subsistence farmers, humanitarian workers, scientists, policymakers, and donors â to both understand and experience the difficulty and risks involved related to decision-making in a complex and uncertain future. The dozen Task Force experts who contributed to the report represent academic institutions, humanitarian organization, other non-governmental organizations, and game design firms with backgrounds ranging from climate modeling and anthropology to community-level disaster management and national and global policymaking as well as game design.Red Cross/Red Crescent Climate Centr
Learning in evolutionary environments
Not availabl
The Importance of Social and Government Learning in Ex Ante Policy Evaluation
We provide two methodological insights on \emph{ex ante} policy evaluation
for macro models of economic development. First, we show that the problems of
parameter instability and lack of behavioral constancy can be overcome by
considering learning dynamics. Hence, instead of defining social constructs as
fixed exogenous parameters, we represent them through stable functional
relationships such as social norms. Second, we demonstrate how agent computing
can be used for this purpose. By deploying a model of policy prioritization
with endogenous government behavior, we estimate the performance of different
policy regimes. We find that, while strictly adhering to policy recommendations
increases efficiency, the nature of such recipes has a bigger effect. In other
words, while it is true that lack of discipline is detrimental to prescription
outcomes (a common defense of failed recommendations), it is more important
that such prescriptions consider the systemic and adaptive nature of the
policymaking process (something neglected by traditional technocratic advice)
Avoiding Braess' Paradox through Collective Intelligence
In an Ideal Shortest Path Algorithm (ISPA), at each moment each router in a
network sends all of its traffic down the path that will incur the lowest cost
to that traffic. In the limit of an infinitesimally small amount of traffic for
a particular router, its routing that traffic via an ISPA is optimal, as far as
cost incurred by that traffic is concerned. We demonstrate though that in many
cases, due to the side-effects of one router's actions on another routers
performance, having routers use ISPA's is suboptimal as far as global aggregate
cost is concerned, even when only used to route infinitesimally small amounts
of traffic. As a particular example of this we present an instance of Braess'
paradox for ISPA's, in which adding new links to a network decreases overall
throughput. We also demonstrate that load-balancing, in which the routing
decisions are made to optimize the global cost incurred by all traffic
currently being routed, is suboptimal as far as global cost averaged across
time is concerned. This is also due to "side-effects", in this case of current
routing decision on future traffic.
The theory of COllective INtelligence (COIN) is concerned precisely with the
issue of avoiding such deleterious side-effects. We present key concepts from
that theory and use them to derive an idealized algorithm whose performance is
better than that of the ISPA, even in the infinitesimal limit. We present
experiments verifying this, and also showing that a machine-learning-based
version of this COIN algorithm in which costs are only imprecisely estimated (a
version potentially applicable in the real world) also outperforms the ISPA,
despite having access to less information than does the ISPA. In particular,
this COIN algorithm avoids Braess' paradox.Comment: 28 page
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