30,540 research outputs found
Reinterpretation of Sieczka-Ho{\l}yst financial market model
In this work we essentially reinterpreted the Sieczka-Ho{\l}yst (SH) model to
make it more suited for description of real markets. For instance, this
reinterpretation made it possible to consider agents as crafty. These agents
encourage their neighbors to buy some stocks if agents have an opportunity to
sell these stocks. Also, agents encourage them to sell some stocks if agents
have an opposite opportunity. Furthermore, in our interpretation price changes
respond only to the agents' opinions change. This kind of respond protects the
stock market dynamics against the paradox (present in the SH model), where all
agents e.g. buy stocks while the corresponding prices remain unchanged. In this
work we found circumstances, where distributions of returns (obtained for quite
different time scales) either obey power-law or have at least fat tails. We
obtained these distributions from numerical simulations performed in the frame
of our approach
Learning and coordinating in a multilayer network
We introduce a two layer network model for social coordination incorporating
two relevant ingredients: a) different networks of interaction to learn and to
obtain a payoff , and b) decision making processes based both on social and
strategic motivations. Two populations of agents are distributed in two layers
with intralayer learning processes and playing interlayer a coordination game.
We find that the skepticism about the wisdom of crowd and the local
connectivity are the driving forces to accomplish full coordination of the two
populations, while polarized coordinated layers are only possible for
all-to-all interactions. Local interactions also allow for full coordination in
the socially efficient Pareto-dominant strategy in spite of being the riskier
one
Scientific Polarization
Contemporary societies are often "polarized", in the sense that sub-groups
within these societies hold stably opposing beliefs, even when there is a fact
of the matter. Extant models of polarization do not capture the idea that some
beliefs are true and others false. Here we present a model, based on the
network epistemology framework of Bala and Goyal ["Learning from neighbors",
\textit{Rev. Econ. Stud.} \textbf{65}(3), 784-811 (1998)], in which
polarization emerges even though agents gather evidence about their beliefs,
and true belief yields a pay-off advantage. The key mechanism that generates
polarization involves treating evidence generated by other agents as uncertain
when their beliefs are relatively different from one's own.Comment: 22 pages, 5 figures, author final versio
Public debates driven by incomplete scientific data: the cases of evolution theory, global warming and H1N1 pandemic influenza
Public debates driven by incomplete scientific data where nobody can claim
absolute certainty, due to current state of scientific knowledge, are studied.
The cases of evolution theory, global warming and H1N1 pandemic influenza are
investigated. The first two are of controversial impact while the third is more
neutral and resolved. To adopt a cautious balanced attitude based on clear but
inconclusive data appears to be a lose-out strategy. In contrast overstating
arguments with wrong claims which cannot be scientifically refuted appear to be
necessary but not sufficient to eventually win a public debate. The underlying
key mechanism of these puzzling and unfortunate conclusions are identified
using the Galam sequential probabilistic model of opinion dynamics. It reveals
that the existence of inflexible agents and their respective proportions are
the instrumental parameters to determine the faith of incomplete scientific
data public debates. Acting on one's own inflexible proportion modifies the
topology of the flow diagram, which in turn can make irrelevant initial
supports. On the contrary focusing on open-minded agents may be useless given
some topologies. When the evidence is not as strong as claimed, the inflexibles
rather than the data are found to drive the opinion of the population. The
results shed a new but disturbing light on designing adequate strategies to win
a public debate.Comment: 31 pages, 7 figure
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network
We study the problem of optimally investing in nodes of a social network in a
competitive setting, where two camps aim to maximize adoption of their opinions
by the population. In particular, we consider the possibility of campaigning in
multiple phases, where the final opinion of a node in a phase acts as its
initial biased opinion for the following phase. Using an extension of the
popular DeGroot-Friedkin model, we formulate the utility functions of the
camps, and show that they involve what can be interpreted as multiphase Katz
centrality. Focusing on two phases, we analytically derive Nash equilibrium
investment strategies, and the extent of loss that a camp would incur if it
acted myopically. Our simulation study affirms that nodes attributing higher
weightage to initial biases necessitate higher investment in the first phase,
so as to influence these biases for the terminal phase. We then study the
setting in which a camp's influence on a node depends on its initial bias. For
single camp, we present a polynomial time algorithm for determining an optimal
way to split the budget between the two phases. For competing camps, we show
the existence of Nash equilibria under reasonable assumptions, and that they
can be computed in polynomial time
The Social Epistemology of Consensus and Dissent
This paper reviews current debates in social epistemology about the relations âbetween âknowledge âand consensus. These relations are philosophically interesting on their âown, but âalso have âpractical consequences, as consensus takes an increasingly significant ârole in âinforming public âdecision making. The paper addresses the following questions. âWhen is a âconsensus attributable to an epistemic community? Under what conditions may âwe âlegitimately infer that a consensual view is knowledge-based or otherwise âepistemically âjustified? Should consensus be the aim of scientific inquiry, and if so, what âkind of âconsensus? How should dissent be handled? It is argued that a legitimate inference âthat a âtheory is correct from the fact that there is a scientific consensus on it requires taking âinto âconsideration both cognitive properties of the theory as well as social properties of âthe âconsensus. The last section of the paper reviews computational models of âconsensus âformation.
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