Problem-solving research

(Response to “Hypothesis-Overdrive”

While I agree with the critique of “hypothesis” by Glass and by Glass & Hall, I believe that basing research funded by the public dollar on “questions” misses the point. What intriques a researcher in the form of a question may be very hard to explain to a layperson, or even a colleague in the same field. I prefer to explain to my students that research proposals must be “problem-solving”. These are typically mechanistic or design/engineering or targeted description (essentially the same as Freedman’s taxonomy of research problems -Freedman, P. (1960). The Principles of Scientific Research. Oxford, Pergamon Press. ), but inevitably the solution to scientific problems involves all three modes of research. Problems are solved by proposed design, hypotheses or search strategies, all of three of which can be collected under the rubric of ‘testable solutions”. Hypotheses are tools for solving problems; and we should dispense with any hypothesis as soon as it no longer is helping us solve the problem. I would suggest that “questions” and “models” (more or elss as described by Glass and Hall) are also tools for problem solving. Scientists should love hypotheses or questions no more than a journeyman loves her hammer.
Now, I suggest, that anyone can come to understand a problem, even if they never understand the hypothesis. Now, problems can be posed as questions, but not all questions identify problems. “How does Toxoplasma infection cause mice to lose their fear of cats?,” refers to a problem but “Does Toxoplasma damage fear-processing neurons in the amygdala” refers to an hypothesis that MIGHT solve the problem. Similarly, we can identify a design-engineering problem as a question (“How can we send humans to Mars and bring them back alive”) or as a statement (“The problem is to send humans to Mars and bring them back alive”). Similarly, we can express the targetted description of the human genome as a questions (“What is the sequence of the human genome?”) or as a mechanistic question (“How can we explain the genetic basis of human disease?) or as search stragety (“Sequencing the human genome should reveals details critical to explaining the genetic basis of human disease”).

IGRa: Introduction to Graduate Research (reading and parsing science)

BCM Term I     IGRa Reading and Parsing Science      Wed 11-12 a.m. Room N901

In this segment of IGR we will learn tools for handling the scientific literature: searching, recording, annotating, reading , interpreting ,critiquing and presenting.   We will analyze one paper in some detail,  Alves-Filho et al. (2010) “Interleukin-33 attenuates sepsis by enhancing neutrophil influx to the site of infection. Nature Medicine 16: 708.

1. Aug 4 Outline of course.  Problem identification. HW: access Pubmed, Endnote,CiteLike, google docs; post 5 citations from Pubmed to CiteUlike; create EndNote .enl file; read OPTEMA chapter (2.2) and send in a 7-bullet OPTEMA analysis of research rotation.

2. Aug 11. OPTEMASM and Reading Strategies; Operations

3. Aug 18  The Typology of Scientific Sentences; students present OPTEMA analyses of figures

4. Aug 25 The One Figure Journal Club;  Instructor presentation  Differentiating Hypotheses and Predictions: students present analyses of text associated with figures

5. Sep 1 (T) Typology of citations      students present analyses of hypotheses

6. Sept 8 .  1 Fig JC   students present analyses of citations

7. Sept 15  1 Fig student presentations (2 presentations)

8 Sept 22  1 Fig JC Student presentations (1 presentations)

Concept mapping tools -listing

Downloadable tools
VUE  (Visual Understanding Environment) – I use this extensively for personal organization, concept mapping, course design, teaching, flow charting.  Very easy to use with just enough but not too many shapes, colors. allows spring loaded edges between nodes.  Works on PC, Mac, Linux. Allows links to url, local documents. Output as VUE, image, html.   Free from Tufts University where they have a very nice set of on-line resources supporting VUE.

Eyeplorer EyePlorer isn’t a concept mapping tool in the sense usually used, but makes use of a cool technology to mine wikipedia for associations with search terms.  These are presented on a map which is a kind of concept map of what we might call the Wikipedia Commons.  See my review.

See discussions and reviews at

Modes of Research

There are four modes of research corresponding to four types of research problems:

  1. Standard problems are those that (appear to) have standard solutions, such as the problem of finding the two roots of a quadratic equation. Researchers seek methodologies such that other problems can be solved by resorting to a set of standard problems.
  2. Exploratory/Discovery problems are those for which too little is known to formulate  critical observations. The critical observation relevant here is a meta-observation about the discipline itself: that too little is known. The problem then is “How can we learn more about this field in a manner that maximizes the accumulation of new critical observations?”
  3. Hypothesis-testing problems are those for which the causal mechanism underlying a critical observation.  There are in modern science two kinds of causation relevant here. (a)  Efficient causation deals with the antecedent mechanism “upstream” of the event in a causal pathway, and with the consequential events proceeding from the event.  Thus we can ask, in the first case, “how X happens” and in the second, “What does X cause”.    (b) “Teleonomic” causation (a term popularized by Ernst Mayr) seeks to understand the functional consequence of an event (such as a genetic mutation) in terms of its ecological or organismic consequences for organisms inheriting it.  It resembles, and is easily confused with teleology, a form of causation excluded from modern science for the last several hundred years.
  4. Design/Engineering  problems pose design criteria that must be satisfied (answered) by design and engineering solutions.

Note that most research involves a mixture of all four modes of research.


OPTEMA[SM]  is a mnemonic device  created by John Rodgers for ordering seven key steps in solving problems:

O = observation. In particular, critical observations are those that (a) are based on ‘facts’ or phenomena judged to be close to factual and (b) are logically tied to critical problems.

P = problem.  In particular, critical problems are (a) based on critical observations and (b) are judged important or significant by the problem solver(s).

T = testable solutions (a) solve or answer the critical problem and (b) make at least one PREDICTION testable using some form of experiment (=controlled observation). Note that a Solution (=hypothesis; design; strategy) is NOT a prediction. The Predicion specifies what will be observed IF (1) the solution is “true” and (2) a certain set of operations are carried out.

E = experiment tests whether under defined conditions the PREDICTION is upheld or not. Experiments will be conducted using one or another set of methods.

M = material and methods are the operationally defined procedures by which a prediction may be assessed.

A = analysis.  This includes evaluation of whether the prediction(s) were upheld; whether the testable ideas were verified or falsified, whether the problem remains critical, whether there are caveats about the experiment or analysis, whether the results generate new critical observations.

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