Hi there, six months have passed since I started to study in Engineering and Policy Analysis Master Program in TU Delft. It is quite the right time to tell and share a bit of what I learn in the major so far.

One semester is divided into two quarter in TU Delft.

In the first quarter, we are given three courses that underlie the foundation of policy analysis. Those three obligatory courses are EPA1101 Understanding International Grand Challenges, EPA1124 Policy Analysis of Multi-Actor Systems, and EPA1315 Data Analytics & Visualization. In addition, we got an advanced presentation bonus course which only counts as pass or fails (0 ECTS).

If I boil down my memory of what I learn in first quarter into a sentence, it will be

“Welcome to the swamp.”

This statement was delivered in the first lecture in one of the courses.

Later on, I found the paper that the lecturer meant.

“In the varied topography of professional practice there is a high, hard ground where practitioners can make effective use of research-based theory and technique, and there is a swampy lowland where situations are confusing ‘messes’ incapable of technical solution. The difficulty is that the problems of the high ground, however great their technical interest, are often relatively unimportant to clients or to the larger society, while in the swamp are the problems of greatest human concern. (Schon, 1983)”

Warren E. Walker, 2000

Policymakers are faced with a rapid change of technology while policymakers are usually *cough* government. Public policymakers have a responsibility to develop and implement policies that have the best chance of improving well-being, health and safety of their citizens.

Policymaking itself is not easy.

There are a lot of reasons why we couldn’t come up with the best solution, cognitive limits, time limits, tractability decision problem and many more. One cannot always find a win-win solution for everyone.

Still, without analysis, important policy choices that based on guts and feelings more often than not produce an aimless result or right solution to the non-existent problem.

That’s one of the reasons I guess the program exists.

Public policy analysis is a rational, systematic approach to making policy choices in the public sector. Its purpose is to assist policymakers in choosing a course of action from among complex alternatives under uncertain conditions. At least, that’s the ideal condition.

Policy analysis, in the end, is a decision aid. A calendar to list all planned activities, a checklist to remind the user, a dog to guide blind people which in the end might be useless if the user just chooses to ignore it. It also needs simplification as complex system contains so many variables and interactions. A prediction would be impossible to be done.

It is worth to note that policy analysis itself falls into a social science which implies human beliefs are influencing the context and the process of analysis itself. One can argue that egg comes first and the other can say that chicken comes first. This two of a simple set of beliefs in policy analysis would lead to a different solution in the end.

Many basic concepts about policy analysis will be informed in EPA1124 Policy Analysis of Multi-Actor Systems. There’s this hexagon of policy analyst role that shows the possibility of a mixed role. Trumpet of uncertainty. Scenario. Resource dependence.

In EPA1101 Understanding International Grand Challenges, I made acquaintance to wicked problems. There are ten characteristics listed in Rittel and Webber paper if you’re really curious about them. There is even paper talking about four key features comprising a “super wicked” problem. As if wicked problem is not enough, now we have super wicked problem.

And EPA1315 was finally offering me the chance to play with something concrete and if you’re like me coming from an engineering background that prefers concrete activity, I hope you can understand it.

I’m not claiming that coding is easy for me. At least you know it’s break and not running while in social science, one incident can be explained than more than one perspective. Feel free to correct me if I’m wrong.

In this course, I also getting more familiar with RStudio as I’ve only heard and not use it actively before. Along the course, I’ve remembered that I’m introduced on how to make a good graph, infographic or beautiful data presentation.

Quarter 2 in a nutshell.

“All models are wrong but some are useful.”

At this quarter, two out of three courses were about modelling.

The first one, introduction to TPM modelling, taught us discrete event simulation and system dynamic. I had to say that it’s tougher than “introduction” sounds. Those two types of modeling are most common used in operational research as policy analysis historically speaking branch out from operational research.

We learned Discrete Event Simulation with Simio as software tool. We used Vensim software as tools to simulate System Dynamics.

The conflicting part is discrete event simulation is using stochastic data to determine the outcome in an accurate way while the system dynamics is using the determined number to see the behaviour (not an accurate outcome).

The goal of modelling is to establish a relation between two systems which is the world and the model. The world is encoded into a model and after it’s run successfully the implication of the model is decoded into the world.

Modelling is a cycle. Starting from conceptualization, specification, verification and validation, and the last step is experiment and interpretation. Take a step back or start to question your existential identity if you meet a dead end.

You can imagine conceptualization is the early phase of making sense of anything. Knowing the number of apple will be x variable and the orange will be y variable. Specification is when you input the numbers into the variables. Verification and validation then happened when you try to check whether you pay as you predict based on the calculation. Experiment and interpretation are when you change the variable like giving discount and try to make more money by selling it again.

The second one is actor and strategy models where we’re taught about comparative cognitive mapping, non-cooperative game theory, cooperative game theory, and social network analysis. There were more models out there but I assume this four is the most common or intuitive to use to explain actor and strategy models. These several models emphasize on several actor involvements with each actor has a set of strategies that can be deployed into the decision arena.

Each of the model answer different questions such as comparative cognitive mapping tries to reveal cognitive perception differences between actors. You can imagine two people argue about the table but one imagines mathematics table and the other imagine wooden table.

Cooperative game theory tries to look for whether it’s a good strategy or bad strategy to join forces with one another. The model might reveal who could help the actor what’s being aspired.

The non-cooperative game theory reveals how each actor respond to several options of action that might be done. You can imagine this as a chess but on an abstract level. Another way to imagine it is to imagine that two football team is playing against each other. When one trying to explore their defensive hole, the opposing team strengthen their defence or try to score more.

Social network analysis is to seek who’s the information broker. You can imagine it as a radio tower that everyone will know the information if the information reaches him/her/a particular organization.

Forming the right question is as crucial as finding the answer.

Now, why all models are wrong? Well, one of the main reasons is because all models have to make an underlying assumption to limit the problem scope. A model is always limited because a model is a representation of a policy analyst’s point of view of the real world. In one way or another, there is always part being left out either time pressure or simply it’s not seen at all.

Some are useful to forecast what might happen although model is not a fortune teller you see. One of the famous case out there is SHELL success story. It’s also useful to simulate what could be done to be closer towards the goal without doing any harm to the real world by experiment.

In this quarter, even though we’re still dealing with wicked problems, I felt more empowered by knowing there are tools provided to analyze and propose. I’m aware there won’t exist a silver bullet to understand all the problems, let alone solve it.

A thing that I had to remind myself in the future: presenting too advanced model might be ineffective. When you try to kill a bird with a cannon, I must say that it’s excessive. It’s amazing though if you can hit the right target.

The third course named intercultural relations and project management. This course title speak clearly for the content itself. We learnt about culture among countries that being measured by culture dimensions. (Damn so-called quantitative scientist). We also learn project management which circulated around group topics such as intergroup process, intragroup process, leadership and management, inclusion and diversity. In this course, there are several workshops about role-play simulation or discussion sessions.

Wrap up

Based on my experience, it promised a lot of new stuff to learn. There are several assignments to work during the semester. I learn GERD case, a conflict between Egypt and Ethiopia over Nile river. Germany nuclear waste disposal plan. I’ve started to question future generation livability when I read the nuclear waste problem. Humanitarian logistics in Bangladesh. HIV-TB coinfection phenomenon in Indonesia. It’s rich of new pieces of information.

For the technical skill part, I learned RStudio, RJAGS, Simio, Vensim, Gephi, Dana, and Gambit. There are others who learn Python too.

PS: I still don’t have any idea where it will lead in the end. You’re welcome to ask me to clarify things that I’ve mentioned here.