1,172 research outputs found
The Evolution of Language Universals: Optimal Design and Adaptation
Inquiry into the evolution of syntactic universals is hampered by severe limitations on
the available evidence. Theories of selective function nevertheless lead to predictions of
local optimaliiy that can be tested scientifically. This thesis refines a diagnostic,
originally proposed by Parker and Maynard Smith (1990), for identifying selective
functions on this basis and applies it to the evolution of two syntactic universals: (I) the
distinction between open and closed lexical classes, and (2) nested constituent structure.
In the case of the former, it is argued that the selective role of the closed class items is
primarily to minimise the amount of redundancy in the lexicon. In the case of the latter,
the emergence of nested phrase structure is argued to have been a by-product of
selection for the ability to perform insertion operations on sequences - a function that
plausibly pre-dated the emergence of modem language competence. The evidence for
these claims is not just that these properties perform plausibly fitness-related functions,
but that they appear to perform them in a way that is improbably optimal.
A number of interesting findings follow when examining the selective role of
the closed classes. In particular, case, agreement and the requirement that sentences
have subjects are expected consequences of an optimised lexicon, the theory thereby
relating these properties to natural selection for the first time. It also motivates the view
that language variation is confined to parameters associated with closed class items, in
turn explaining why parameter confiicts fail to arise in bilingualism.
The simplest representation of sequences that is optimised for efficient
insertions can represent both nested constituent structure and long-distance
dependencies in a unified way, thus suggesting that movement is intrinsic to the
representation of constituency rather than an 'imperfection'. The basic structure of
phrases also follows from this representation and helps to explain the interaction
between case and theta assignment. These findings bring together a surprising array of
phenomena, reinforcing its correctness as the representational basis of syntactic
structures.
The diagnostic overcomes shortcomings in the approach of Pinker and Bloom
(1990), who argued that the appearance of 'adaptive complexity' in the design of a trait
could be used as evidence of its selective function, but there is no reason to expect the
refinements of natural selection to increase complexity in any given case.
Optimality considerations are also applied in this thesis to filter theories of the
nature of unobserved linguistic representations as well as theories of their functions. In
this context, it is argued that, despite Chomsky's (1995) resistance to the idea, it is
possible to motivate the guiding principles of the Minimalist Program in terms of
evolutionary optimisation, especially if we allow the possibility that properties of
language were selected for non-communicative functions and that redundancy is
sometimes costly rather than beneficial
The Promises and Perils of Agent-Based Computational Economics
In this paper I analyse the main strengths and weaknesses of agent-based computational models. I first describe how agent-based simulations can complement more traditional modelling techniques. Then, I rationalise the main theoretical critiques against the use of simulation, which point to the following problematic areas: (i) interpretation of the simulation dynamics, (ii) estimation of the simulation model, and (iii) generalisation of the results. I show that there exist solutions for all these issues. Along the way, I clarify some confounding differences in terminology between the computer science and the economic literature.Agent-based, Simulation, Microsimulation, Computational Economics, Structural Estimation, Economic methodology
Artificial Intelligence and Patent Ownership
Invention by artificial intelligence (AI) is the future of innovation. Unfortunately, as discovered through Freedom of Information Act requests, the U.S. patent regime has yet to determine how it will address patents for inventions created solely by AI (AI patents). This Article fills that void by presenting the first comprehensive analysis on the allocation of patent rights arising from invention by AI. To this end, this Article employs Coase Theorem and its corollaries to determine who should be allowed to secure these patents to maximize economic efficiency. The study concludes that letting firms using AI to create new technologies (as opposed to software companies, programmers, or downstream parties) to obtain the resulting patents is the optimal policy
Adaptive networks for robotics and the emergence of reward anticipatory circuits
Currently the central challenge facing evolutionary robotics is to determine
how best to extend the range and complexity of behaviour supported by evolved
neural systems. Implicit in the work described in this thesis is the idea that this
might best be achieved through devising neural circuits (tractable to evolutionary
exploration) that exhibit complementary functional characteristics. We concentrate
on two problem domains; locomotion and sequence learning. For locomotion
we compare the use of GasNets and other adaptive networks. For sequence learning
we introduce a novel connectionist model inspired by the role of dopamine
in the basal ganglia (commonly interpreted as a form of reinforcement learning).
This connectionist approach relies upon a new neuron model inspired by notions
of energy efficient signalling. Two reward adaptive circuit variants were investigated.
These were applied respectively to two learning problems; where action
sequences are required to take place in a strict order, and secondly, where action
sequences are robust to intermediate arbitrary states. We conclude the thesis
by proposing a formal model of functional integration, encompassing locomotion
and sequence learning, extending ideas proposed by W. Ross Ashby.
A general model of the adaptive replicator is presented, incoporating subsystems
that are tuned to continuous variation and discrete or conditional events.
Comparisons are made with Ross W. Ashby's model of ultrastability and his
ideas on adaptive behaviour. This model is intended to support our assertion
that, GasNets (and similar networks) and reward adaptive circuits of the type
presented here, are intrinsically complementary. In conclusion we present some
ideas on how the co-evolution of GasNet and reward adaptive circuits might lead
us to significant improvements in the synthesis of agents capable of exhibiting
complex adaptive behaviour
A developmental model of number representation
We delineate a developmental model of number representations. Notably, developmental dyscalculia (DD) is rarely associated with an all-or-none deficit in numerosity processing as would be expected if assuming abstract number representations. Finally, we suggest that the "generalist genesâ view might be a plausible - though thus far speculative - explanatory framework for our model of how number representations develo
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
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