On Modeling, 2

We build models because we want to highlight the most relevant factors of the circumstances. In a broad sense, language, music, painting are all different forms of modeling: they stress certain aspects of reality by ignoring the rest.

Models must be useful. Unlike simplicity and generality, usefulness is trickier to assess. The challenge arises from the parallel of two worlds: the real and the model world; so there is a gap one must be able to cross.

The main idea of modeling is that, if we isolate the substantive factors of a system into a model, then the outcomes generated by the model should also find counterparts in the real world. Or, if we observes certain outcomes in the real world, and the model produces similar outcomes, then these substantive factors should be determinants in the real world, too.

Both arguments rely on inductive inference. In general, the model and real world should share similarities in  structure, dynamics, and outcomes. Yet these similarities are no guarantee that two worlds move in lock step. Cases abound of otherwise. Thus, relevance has to be taken by faith, not by the logic reasoning. There are gaps that one must be willing to cross.

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On Modeling, 1

All disciplines use models. So what is a model? It is a useful abstraction of reality. It is an abstraction because it does not intend to recreate the real world in a one-to-one scale. Rather, it distills the essence of a situation so that it can be readily deployed in other similar situations.

One reason we need models is because we have limited cognitive capacity—you cannot keep track of all the details all the time. Modeling is a way to filter out irrelevance and to focus on what matters. So models are the lens we use to see the world we want to see. And simplicity is the first criterion.

The second criterion is generality. We don’t want to build a new model for every single situation; that would defeat the very purpose of modeling. Rather, we would like our models to have sufficient generality, so that we can apply the same models to different situations with limited modifications.

2017-01-08 12.49.15 HDR

Plan month 16.251.

[LIFE]
    build 10h routine: learn 1h, exercise 1h, work 6h, read 1h, and write 1h;
    write a post per day;
    find C a job;
    home deco: finish order furniture, by 1.31.2017;
    call mom once a week;
    plan the trip next month;
    car service, 4h;
    clean up the closet, 4h;
    clean up the house, 4h;
    clean up the mails, 2h;

[HEALTH]
    pickup contact lens;
    research Lasik surgery;
    get rid of coffee, ham, alcohol, and overeating;

[FINANCE]
    upgrade money market saving account;
    cancel macy’s credit card. \$4 fee per month;
    buy oliver black division II field jacket (M) by rag \& bones;
    change the phone plan, 2h;
    cancel Cox. \$97 per month;

[SOCIAL]
    try a new thing, broaden experiences, once a week;
    meet people from MEETUP, REDIT, LINKEDIN;
    go basketball;
    go to econ seminar once a month;

[WORK]
    learn: Corbe, 2h per day, 100hrs;
    MRF: follow up the submission;
    SMI: work out the model, email Liang, 4w;
    MHS: plan for numerical study, position the paper, 2day;
    TQ2: revision, weekly meeting, .5h*3;
    POMS: review, 6h;
    Encro: 1w for editing;
    submit the abstract meeting for POMS in Seattle 1h;
    go through all the printed papers in the garage .5h

[SCHOOL]
    remove the requirement for M239, revise the requirement as capstone type, 4h;
    review hiring, 2h;
    EC meeting, 2 days;
    hiring meeting, 1.20;
    merit:  ask B. to remind silent. The trouble of Rv.;
    ask WSZ PHD in markeitng for lunch/coffee;

TQ: major revision

From the reviewer:
" I enjoyed reading this paper, and find its topic interesting. I also appreciate the approach taken by the authors, which combines an empirical study, a modelling effort and corresponding analysis, and finally managerial recommendations which are of real practical interest. I do have a few comments on the interpretation of the results in the empirical section of the paper, some assumptions in the modelling framework, and the results of the numerical section. However, if these comments could be addressed, then I believe that this paper would make a nice, comprehensive, contribution to the literature."