Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data

Biological Theory 12 (2):99-111 (2017)

Authors
Beckett Sterner
Arizona State University
Abstract
Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision and ambiguity that are key to designing effective solutions for generating big data about biodiversity. We focus on the importance of theory-dependence as a source of ambiguity in taxonomic nomenclature and hence a persistent challenge for implementing a single, long-term solution to storing and accessing meaningful sets of biological specimens. We argue that ambiguity does have a positive role to play in scientific progress as a tool for efficiently symbolizing multiple aspects of taxa and mediating between conflicting hypotheses about their nature. Pursuing a deeper understanding of the trade-offs and synthesis of precision and ambiguity as virtues of scientific language and communication systems then offers a productive next step for realizing sound, big biodiversity data services.
Keywords Big data   Cognitive pragmatics   Concept taxonomy   Data aggregation   Knowledge representation and reasoning   Nomenclature
Categories (categorize this paper)
ISBN(s)
DOI 10.1007/s13752-017-0259-5
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

Our Archive
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

The Structure of Scientific Revolutions.Thomas S. Kuhn - 1962 - University of Chicago Press.
Meaning and Relevance.Deirdre Wilson & Dan Sperber - 2012 - Cambridge University Press.

View all 15 references / Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Issues in Data Management.Sharon S. Krag - 2010 - Science and Engineering Ethics 16 (4):743-748.
Data Models and the Acquisition and Manipulation of Data.Todd Harris - 2002 - Philosophy of Science 70 (5):1508-1517.
A Taxonomy of Errors for Information Systems.Giuseppe Primiero - 2014 - Minds and Machines 24 (3):249-273.
Data Fusion with Probabilistic Conditional Logic.Jens Fisseler & Imre Fehér - 2010 - Logic Journal of the IGPL 18 (4):488-507.
Mental Models in Data Interpretation.Clark A. Chinn & William F. Brewer - 1996 - Philosophy of Science 63 (5):S211-S219.
Mental Models in Data Interpretation.Clark A. Chinn & William F. Brewer - 1996 - Philosophy of Science 63 (3):219.
What Should Be the Data Sharing Policy of Cognitive Science?Mark A. Pitt & Yun Tang - 2013 - Topics in Cognitive Science 5 (1):214-221.

Analytics

Added to PP index
2017-09-23

Total views
68 ( #113,234 of 2,244,034 )

Recent downloads (6 months)
43 ( #15,335 of 2,244,034 )

How can I increase my downloads?

Downloads

My notes

Sign in to use this feature