Confirmation of Ecological and Evolutionary Models

ثبت نشده
چکیده

This paper concerns hypothesis testing and confirmation in evolutionary and ecological theory. I outline specific criteria used in evaluating evidence for theories, and demonstrate the use of each criterion through examples from various branches of evolutionary biology and ecology. The philosophical discussions in Roughgarden (1983), Strong (1983), Simberloff (1983), and Quinn and Dunham (1983), which focus on a Popperian approach to theory testing and acceptance, present some important issues in the testing of evolutionary and ecological explanations. I find that imprecision of criteria of testing and confirmation is the weakest point in these discussions. As an alternative to a Popperian approach (as defended by, e.g., Simberloff 1983), and to other approaches commonly cited by biologists (e.g., J. Platt’s “strong inference”), I suggest a new description of confirmation that includes a detailed classification of the ways in which a theory may be confirmed (see, e.g., Oster and Wilson 1978 for an endorsement of Platt’s 1964 paper). Roughgarden (1983) proposes that one establishes an empirical fact in science “by building a convincing case for that fact.” What counts as a convincing case depends, according to Roughgarden, on “common sense and experience” (pp. 583–584). As Strong (1983) rightly points out, the appeal to common sense is problematic; common sense may not be “common” to all scientists concerned, and it says nothing about testing. Strong’s solution to problems of confirmation and testing is not much of an improvement, though: “our

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Ecological Niche Modeling of Mountain Vipers from the Raddei Clade in Iran, Caucasus and Eastern Turkey

Mountain vipers of the genus Montivipera, generally, and the species of the Raddei clade, specifically, are interesting examples of species neo-endemism in Iran, Anatolia, and the Caucasus. Given the critical conservation status of these species, it is necessary to identify their suitable habitats for prioritizing conservation measures. We modeled ecological niche of each species based on four ...

متن کامل

The role of environments with extreme ecological conditions in the reductive evolutionary development processes of animal

Different groups of animals show phenotypic characters, which have been resulted by the reductive phenomena. The examples are the absence of pigmentation; dwindle of eyes in some cave-living animals, and also the absence of scale in some fishes. These characters are often leaded to evolution of new species with special adaptation that is so called "Regressive evolution". The reductive phenomena...

متن کامل

Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network

Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Pro...

متن کامل

Soft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors

Soft computing models based on intelligent fuzzy systems have the capability of managing uncertainty in the image based practices of disease. Analysis of the breast tumors and their classification is critical for early diagnosis of breast cancer as a common cancer with a high mortality rate between women all around the world. Soft computing models based on fuzzy and evolutionary algorithms play...

متن کامل

SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011