Modelling in Opasnet

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Question

How should modelling be done in Opasnet in practice? This page should be a general guidance on principles, not a technical manual for using different tools.

Answer

For a general instruction about contributing, see Contributing to Opasnet.

Relationship of Answer and Rationale

All variable pages should have a clear question and a clear answer. The answer should typically be in a form of a data table that has all indices (explanatory columns) needed to make the answer unambiguous and detailed enough. If the answer table is very large, it might be a bad idea to show it on the page; instead, a description is shown about how to calculate the answer based on Dependencies and Rationale, and only a summary of the result is shown on the page; the full answer is saved into Opasnet Base.

The answer should be a clear and concise answer to the specific question, not a general description or discussion of the topic. The answer should be understandable to anyone who has general knowledge and has read the question.

In addition, the answer should be convincing to a critical reader who reads the following data and believes it is correct:

The Rationale section of the page.
The Answer sections of all upstream variables listed in the Dependencies section.
In some cases, also downstream variables may be used in inference (e.g. in hierarchical Bayes models).

It should be noted that the data mentioned above should itself be backed up by original research from several independent sources, good rationale etc. It should also be noted that ALL information that is needed to convince the reader should be put into the places mentioned and not somewhere else. In other words, when the reader has read the rationale and the relevant results, (s)he should be able to trust that s(he) is now aware of all such major points related to the specific topic that have been described in Opasnet.

This results in guidance for info producers: if there is a relevant piece of information that you are aware of but it is not mentioned, you should add it.

Indices of the data table

The indices, i.e. explanatory columns, should match in variables that are causally connected by a causal diagram (i.e., mentioned in Dependencies). This does not mean that they must be the same (as not all explanations are relevant for all variables) but it should be possible to see which parts of the results of two variables belong together. An example is a geographical grid for two connected variables such as a concentration field of a pollutant and an exposure variable for a population. If the concentration and population use the same grid, the exposure is easy to compute. However, they can be used together with different grids, but then there is a need to explain how one data can be converted into the other grid for calculating exposures.

Increasing preciseness of the answer

This is a rough order of emphasis that could guide the work when starting from scratch and proceeding to highly sophisticated and precise answers. The first step always requires careful thinking, but if there are lots of easily available data, you may proceed through steps 2 - 4 quickly; with very little data it might be impossible to get beyond step 3.

Describe the variables, their dependencies and their indices (explantaions) to develop a coherent and understandable structure and causal diagram.
Describe the variables as point estimates and simple (typically linear) relations to make the first runnable model. Check that all parts make sense. Check that all units are consistent. Whether all values and results are plausible is desirable but not yet critical.
Describe the variables as ranges in such a way that the true value is most likely within the range. This is more important than having a very precise range (and thus higher probability not covering the truth). This may result in vague conclusions (like: It might be a good idea to do this, but on the other hand, it might be a bad idea). But that's exactly how it should be: in the beginning, we should be uncertain about conclusions. Only later when we collect more data and things become more precise, also the conclusions are clarified. At this step, you can use sensitivity analyses to see where the most critical parts of your model are.
The purpose of an assessment model is to find recommendations for actions. Except for the most clear cases, this is not possible by using variable ranges. Instead, probability distributions are needed. Then, the model can be used in optimising, i.e., finding optimal decision combinations.
When you have your model developed this far, you can use the Value of information analysis (VOI analysis) to find the critical parts of your model. The difference to a sensitivity analysis is that a VOI analysis tests which parts would change your recommendation, not which parts would change your estimate of outcome. Often the two analyses point to the same direction, but a VOI analysis is more about what you care, while a sensitivity analysis can be performed even if no explicit decision has yet been clarified.

Rationale

A draft based on own thinking. Not even the topics are clear yet.

Montako lukiolaista tarvitaan korvaamaan 1 asiantuntija? Laske tehokas asiantuntijan opiskeluaika ja se osuus joka siitä tarvitaan ratkaisemaan kyseinen ongelma

Arvaus: 10. Asiantuntijat halveksivat pinnallista tietoa ja heillä on syvällistä. Mikä ero? Kytkennät. Jos 2 asiaa on mahdollisia mutta ei yhtaikaa, asiantuntija tunnistaa tämän mutta maallikko ei. Lukiolaisista saadaan asiantuntijoita opettamalla heille menetelmä kuvata kytkentöjä. Sen jälkeen kaiken tiedon ei tarvitse enää olla 1 ihmisen päässä.

Ihmisten on vaikea hahmottaa, että lukuisia ongelmia voidaan ratkoa kerralla samalla menetelmällä. Sen sijaan yhden ongelman ratkaisuun voidaan motivoida suuria joukkoja, jos aihe on heille tärkeä. Pitäisikö siis löytää se yksi tärkeä asia? Muut sitten alkavat ratketa vahingossa.

Vaikeaa on myös nähdä metatason kysymyksiä eli järjestelmää tai itseä osana isompaa rakennetta, jonka puitteissa ovat myös mahdolliset maailmat ja jonka sisältä ratkaisut löytyvät.

Mielikuvituksen jaloin laji on kuvitella hyviä asioita, jotka voisivat olla mutta eivät ole, sekä niiden ei-olemisen ja olemisen välistä polkua.

Tieteellinen tiede on kuin amerikkalainen unelma: tieteen menetelmin tehdään riittävästi läpimurtoja jotta joka sukupolvelle riittää omat menestystarinansa ja idolinsa, mutta käytännössä tieteen metodi on liian kaukana tutkijan arjesta jotta se todella siihen vaikuttaisi. Niinpä tutkijat elävät illuusion varassa kuten amerikkalaisetkin, ja puurtavat vailla mahdollisuuksia todellisiin tavoitteisiinsa jotka ovat suurempia ja vaikuttavampia kuin mihin tieteen järjestelmä antaa mahdollisuuksia. Tutkijoiden aika ja resurssit menevät 2 asian miettimiseen: mistä saan rahaa ja miten saan ajatuksiani julkaistuksi. Sen sijaan ajatustensa itsensä kehittämiseen on aina liian vähän aikaa. Niinpä ei vain tavoitteet vaan myös kyvyt ovat suuremmat kuin mihin järjestelmä taipuu. Parhaiten pärjäävät toimitusjohtajatyypit, jotka osaavat organisoida rahankeruun, julkaisemisen ja instituutiot oman mielenkiintonsa kohteisiin.

Keywords

References

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