What is a computational constraint?

Date

2016-01-01

Author

Bozşahin, Hüseyin Cem

Metadata

Show full item record

Item Usage Stats

172
views

0
downloads

The paper argues that a computational constraint is one that appeals to control of computational resources in a computationalist explanation. Such constraints may arise in a theory and in its models. Instrumental use of the same concept is trivial because the constraining behavior of any function eventually reduces to its computation. Computationalism is not instrumentalism. Born-again computationalism, which is an ardent form of pancomputationalism, may need some soul searching about whether a genuinely computational explanation is necessary or needed in every domain, because the resources in a computationalist explanation are limited. Computational resources are the potential targets of computational constraints. They are representability, time, space, and, possibly, randomness, assuming that ‘BPP = BQP?’ question remains open. The first three are epitomized by the Turing machine, and manifest themselves for example in complexity theories. Randomness may be a genuine resource in quantum computing. From this perspective, some purported computational constraints may be instrumental, and some supposedly noncomputational or cognitivist constraints may be computational. Examples for both cases are provided. If pancomputationalism has instrumentalism in mind, then it may be a truism, therefore not very interesting, but born-again computationalism cannot be computationalism as conceived here.

URI

https://hdl.handle.net/11511/77506
https://link.springer.com/chapter/10.1007/978-3-319-23291-1_1

Collections

Unverified, Article

Suggestions

OpenMETU
Core

Application of impulsive deterministic simulation of biochemical networks via simulation tools Purutçuoğlu Gazi, Vilda (2017-01-01) In order to understand the possible behaviour of biochemical networks, deterministic and stochastic simulation methods have been developed. However in some cases, these methods should be broaden. For oxii.rupie, if the biochemical system is subjected to the unexpected effects causing abrupt changes in the network, the ordinary simulation algorithms cannot capture these impulsive expressions. In 1.1) is study, we select, 1.1 ic simulations lools. specifically, CO PA ST and Systems Biology Toolbox for MATLAR ...
Maximally Permissive Hierarchical Control of Decentralized Discrete Event Systems SCHMİDT, KLAUS WERNER; Schmidt, Klaus Verner (2011-04-01) The subject of this paper is the synthesis of natural projections that serve as nonblocking and maximally permissive abstractions for the hierarchical and decentralized control of large-scale discrete event systems. To this end, existing concepts for nonblocking abstractions such as natural observers and marked string accepting (msa)-observers are extended by local control consistency (LCC) as a novel sufficient condition for maximal permissiveness. Furthermore, it is shown that, similar to the natural obse...
Fuzzy neural network learning method for time series analysis using multivariate autoregression Sisman, NA; Alpaslan, Ferda Nur (1998-11-13) This paper describes how temporal fuzzy neural network model proposed in [4] can be applied to time series analysis when a multivariate autoregressive model is constructed. The fuzzy multivariate autoregression procedure is described first, then the temporal fuzzy neural network model using this procedure is presented.
Deterministic modeling and inference of biological systems Seçilmiş, Deniz; Purutçuoğlu Gazi, Vilda; Department of Bioinformatics (2017) The mathematical description of biological networks can be performed mainly by stochastic and deterministic models. The former gives more information about the system, whereas, it needs very detailed measurements. On the other hand, the latter is relatively less informative, but, the collection of their data is easier than the stochastic ones, rendering it a more preferable modeling approach. In this study, we implement the deterministic modeling of biological systems due to the underlying advantage. Among ...
Using constrained intuitionistic linear logic for hybrid robotic planning problems Saranlı, Uluç (2007-04-14) Synthesis of robot behaviors towards nontrivial goals often requires reasoning about both discrete and continuous aspects of the underlying domain. Existing approaches in building automated tools for such synthesis problems attempt to augment methods from either discrete planning or continuous control with hybrid elements, but largely fail to ensure a uniform treatment of both aspects of the domain. In this paper, we present a new formalism, Constrained Intuitionistic Linear Logic (CILL), merging continuous...

Citation Formats

H. C. Bozşahin, “What is a computational constraint?,” pp. 3–16, 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/77506.