## Drift in politics – policies and voter opinions

Last week I finished the Coursera course on Model Thinking and among the final lesson were discussed the basics of game theory. Game theory was no new topic to me. I believe many have at least heard of it, but really applying it was an advancement. Finding the topic interesting, I dug a bit deeper and found a web course on game theory from Yale consisting of Youtube videos accompanied by transcripts and training material.

Median voter theorem

The third video lecture of the Yale course introduces the median voter theorem that roughly states that under certain conditions, politicians will tend to drift towards the political center in their opinions and supported policies. This drift is eventually their best response when they iteratively analyze their own and their opponents’ possible strategies. The theorem also implies that the media voter holds the decisive vote and, consequently, gets his will. The required conditions for the median voter theorem to hold are that the voters are symmetrically distributed on both sides of the centrist policies, the voters have so called single-peaked preference functions and there are a relatively small number of candidates competing for votes. For a large number of candidates a single extremist could get more votes than any of the dozen others fighting against each other at the the crowded political center. In addition, the median voter would not cast the decisive vote.

A short introductory article on the median voter theorem is offered by the University of Oregon. The theorem has its limitations, but even in its most basic form it offers a good start for analyzing election results, policy options and motives for implementing policies.

Median voter theorem in parliamentary politics

The median voter theorem (the concept was introduced by Harold Hotelling already in 1929) is relatively simple, but, as mentioned in the article from the University or Oregon, has interesting practical implications.  Already some years before the financial crisis of 2008, political parties with more extreme agenda were gaining on popularity and visibility.  A good example of this is the surge of the Finns Party in Finland, as illustrated by the graphic below. (The graphic is based on data from Statistics Finland. Only the largest parties having seats in the Finnish parliament are represented, covering at least 97% of the votes cast each year.)

According to the median voter theorem, there’s a third explanation. Since the theorem states that, under the assumptions (which I expect to hold reasonably well here), the decisive vote comes from the median voter, the Finns Party must have come closer in its policies to the three large parties or the median voter’s opinions had drifted towards the Finns Party. As the Finns Party profiled itself as a critic of EU-driven legislation, relatively free immigration and a supporter of conservative family values, being clearly apart from the three larger parties, the latter explanation seems more plausible. This is also supported by the fact that, based on the view one gets from the newspaper headlines today, SDP, KESK and KOK have drifted towards the Finns Party when it comes to EU and immigration, likely to redeem some of their lost voters. Especially the current crisis in the Middle-East and the following waves of refugees have made the leaders of the other large parties less eager to promote relatively free immigration and the on the EU level proposed refugee quotas per member country.

Drifting parties and voters

The median voter theorem offers two interesting observations. One, parties and politicians tend to drift towards one another, since the decisive vote is cast by the median voter, not by the one lying nearer to the extremes of the political spectrum and in any case voting for the party nearest to his opinions. Second, the ideological political center may drift, and parties previously positioned at the center may find themselves suddenly farther away from it, as may have been the case in the Finnish parliamentary election of 2011.

By the end of the year 2015, the Finns Party had lost almost 50% of its support after the parliamentary election in the Spring of 2015, being just below 10% in November 2015. This might be explained, as discussed in the article behind the link, by the unpopular decisions the Finns Party has had to accept as compromises between the parties in the government. This may have lead their immigrant critical supporters move to other parties, since the Finns Party’s stance does not differ that much from the other large parties and is milder than that of some other parties not represented in the Finnish parliament. Thus, by accepting policies that are ideologically positioned between them and the other governmental parties, the Finns Party may have drifted, at least in the eyes of their former voters, farther away from the current political center and closer to the other large parties. This drift may have cost them many voters who have turned to more extremely positioned parties. At the same time the Finns Party is competing for votes with the other large parties due to the four having drifted closer to one another. This makes life more difficult for all four parties since there are more takers for the same votes.

## Skill or luck – on outcomes, rewards and fairness 2/2

Who gets the prize?

In my previous post I showed how skill and luck play a role in most activities and why it is important to distinguish between the two and their impact on outcomes. I also argued that evaluating outcomes and awarding people only on that basis is not always right. Yet, we often evaluate outcomes, since they are often easy to grasp and measure. However, as already mentioned, if an individual’s or organization’s impact on a specific outcome is negligible in comparison to the impact that luck has, evaluation solely based on outcome is not fair. The individual should rather be evaluated based on his adhering to the agreed process used to reach the outcome, as previously discussed. Also, among top performers luck, paradoxically, plays a relatively larger role in deciding outcomes. Therefore, we should in evaluating performance and providing merit pay attention to the three following points:

1. Level of personal influence
2. The process used to reach the outcome
3. Level of competition

If we have no or very little personal influence on our success or failure, we should not be overly rewarded for good outcomes, nor punished or left completely unrewarded when the outcomes are less favorable. As mentioned before, luck dominated events might be shoved towards the skill dominated end of the spectrum, but this requires honing our skills and executing the event so as to maximize our impact. In this case, even if luck still dominates the outcome, we will have done our best to tilt the scales to our benefit; had we not followed the optimal process, the outcome would have been even worse. This implies that the correct process of working towards a goal and becoming proficient in that process should be merited, although measuring this might be more complicated than simply observing the outcome.

Finally, we should always observe the level of competition. If a person finishes last in the hundred meter dash finals in the Olympics, he is not a failure in sports and hardly beaten by the casual sprinter. Since luck has a larger role in defining the exact outcome at the top level, especially here we should give rewards for effort and process, not for the outcome. Of course outcomes do matter and they should be given merit. After all, if an outcome gets no merit, why would anyone go through the process required to reach that outcome? We just have to pay attention to the division of merit: as the pressure of reaching good outcomes and the desire for the prize get too high, the correct process may not be adhered to anymore, making the achieved outcomes also questionable.

Bonus or layoff, either way deserved?

As a practical example on rewarding individuals in a group of top performers I raise the rank and yank used by some companies to encourage their employees for better performance. The purposefulness of this policy is easily evaluated with the skill-luck model.

With rank and yank I refer to the practice where employees are annually ranked into multiple categories with fixed percentage quotas, and their future career development and remuneration is category dependent. For example, 10% are evaluated as top performers and they are given above average bonuses and promoted to more demanding position, 80% are average and receive the average bonus while keeping their current job and 10% are below average and have to be fired. Here we have at least three problems: role of skill and luck in an outcome, paradox of skill and the correctness of the evaluation. I will disregard the correctness of evaluation from further discussion, since it is a separate topic, but it is obvious that if the results of an evaluation are not correct or are inconsistent with other evaluations, any decision based on that becomes questionable.

As mentioned before, if the outcome of an individual’s work is largely dominated by luck, firing that individual due to poor outcomes hardly seems justified, assuming that the individual has tried to maximize the potential outcome to the best of his abilities. Also, if the individual belongs to a group of talented individuals, luck is bound to have a larger effect on the exact individual performance inside the group, again making the following gratification and firing of employees questionable.

Companies are reviewing and changing their evaluation and reward systems to help their employees learn and improve their skills constantly. For example, General Electric is using an mobile application that enables the supervisor to encourage and give positive feedback for  positive actions while asking to consider changing less desired ones. Here we see how placing the work process under focus might be the way how personal performance is evaluated and remunerated in the future.

Ain’t I lucky being so skillful

As a final thought a few words on the division of well-being on the global level since, here again, it is a question of skill and luck.

Nobody can choose the family and society to which they are born, nor can a person choose the time to which he is born. Yet, based on family ties and structure of the society, the easily accessible life and career paths are limited. A society offering good healthcare, public education for all and a stable government is much more likely to bring about successful individuals than a society plagued with high child mortality, low level of education and turbulent politics. Against this background, it makes me think what is the obligation of the richer countries to assist the poorer ones to reach higher standards of living? What is our obligation as individuals living in the richer countries to help those living in the poorer ones?

If my success is mostly dictated by luck, by having been born in the right country at a good time to the right parents, how much of the ensuing well-being belongs to me and how much should I share with others? I might claim that my success in studies and at work are a result of my own efforts, but they are also based on both nature and nurture, on my education (broadly understood) and genes, both of which are outside factors, or luck. This notion contradicts the definition of skill in the beginning of part one of this post, where I defined DNA and level of proficiency attained through education and practice to be individual attributes. As it seems, drawing a line between personal attributes and external factors is not easy. This makes the final distinction between skill and luck less clear, giving all the more reason for us to think about it and try to reach a fair solution.

## Skill or luck – on outcomes, rewards and fairness 1/2

Coming towards the end of my Coursera course on model thinking, I was introduced to the concepts of random walks and skill-luck model. Especially the skill-luck model appealed to me as it can be used to explain success on a personal and organizational level and predict, if success is due to skill or luck and if success is lasting. The following is based on the Coursera course Model Thinking by Scott E. Page and the course readings, mainly on How to Entangle Skill and Luck from Michael Mauboussin at Legg Mason Capital management from July 15th 2010. I have split my text into two parts to make the individual posts shorter.

The skill-luck model

The skill-luck model states that an outcome is based, with varying degree, on both skill and luck. Here skill denotes the knowledge and ability of an individual or organization to steer the future events in to the desired direction. Luck, on the other hand, incorporates the noise, random effects, that affect the outcome and cannot be controlled by the individuals or the organization.

To make the definitions more concrete we could say that the one hundred meter dash is mostly skill based, as the winner is based on the speed of the individual, and the speed is mostly dependent on strength, running technique, reaction time and other things that are either inherited in the DNA or a result of hard training and therefore dependent on the individual.

An example of a pure luck event is lottery. One cannot practice or have since birth any advantage that could help in choosing a winning number (clairvoyance excluded). The winning number is solely based on chance, or luck, as people often say.

A general formulation for the skill-luck model, presented also at the Coursera course by Mr. Page, is

Outcome = A*Luck + (1-A)*Skill

where A is constant between zero and one. The formula shows that, depending on the event, luck or skill may dominate, or they may be of roughly equal importance.

Why use the skill-luck model?

The skill-luck model has relevance on both personal and organizational level. In the society, most people arguably want to be successful, and so do organizations, composed of those very people. Some end up being successful, some not. Sometimes success is lasting; sometimes it barely greets us before already taking part.

Success is often looked up to and merited, while failure may be frowned upon or even punishable. Yet, if success or failure is based more on luck than skill, we should be careful in giving praise and delivering punishment. If success and failure take place randomly, it would imply that the accompanied fortunes and fame are provided without, or due to little, personal merit.

Identifying whether something is luck or skill dominated serves at least five purposes.

1. Ability to assess outcomes.
2. Anticipating outcomes and regression to the mean.
3. Giving guidance where we are most likely to be misled.
4. Fair evaluation and appraisal, which also promote learning.
5. More optimal resource distribution.

Assessing outcomes

With the skill-luck model we can assess, whether something is based on skill or luck. A good rule of thumb is, that you cannot lose on purpose in an activity that is dominated by luck, or at least losing on purpose is very difficult. An indicator of a skill dominated activity are lasting streaks. If a person consistently beats others and provides well above average performance consistently, providing that the observed sample is statistically representative, the event is likely to be skill dominated. Not all skilled people have streaks of success, but most streaks are done by skillful people, since the probability of streaks by chance is so low. A good example of a long-lasting streak is the already mentioned hundred meter dash, which has been dominated by Usain Bolt since 2008.

Another method for evaluating the role of skill and luck is to establish the base rate and compare it against actual observations. For example, when examining the role of skill in winning sport matches, the base rate would be the equivalent of a coin toss experiment, when the events consist of teams or individuals going one-on-one. If the group’s variance in the observed performance (for example percentage of games won) is much larger than the variance of the coin toss alternative (winning a game has a 50/50 chance), some contestants do consistently better than odds would have it, and so skill plays a relatively large role in the event. Yet, streaks and clusters can also be random. For example, if we throw a coin long enough, we would expect to get 20 heads in a row. The difference between luck and skill is also in the frequency of such streaks, which are achieved more often by skilled individuals, although they might be helped by some luck to keep the streak going on.

Here, in an MS Excel file,  is an example of a 6-game league between four teams (the method is described by Mauboussin in his paper). Each team plays 2 games against every other team. 1 in the table in the Excel file means that a team won the specific game, 0 indicates the losing team. As we can see from the calculations, skill would seem to play a large role in this specific game, where team 1 has been unbeatable. To ascertain such results in real life, large enough samples (also temporally) have to be ensured, which is not the case with this example, but it illustrates the point.

Anticipating outcomes and regression to the mean

After having identified the role of luck and skill in a specific event, we are more ready to anticipate outcomes of events. If an event is skill dominated, the most skillful are going to man the top positions. If the event is dominated by luck, anyone could win and predicting the top performers, not to mention the winner, becomes difficult. In a skill dominant event it is reasonable to expect that the most skillful will be the best performers, while the consistency of top performance is an indicator of a skill dominant event. This notion is not circular logic, rather it states that first we have to observe, if an event is skill dominant, as we have done in the previous paragraph. After enough observations have been done and the role of skill determined, we can predict, that in skill dominant events current success should be followed by future success, even if we have only few observations from individual performance.

The dominance of luck or skill has interesting consequences for anticipating outcomes. If an event is dominated by skill, it is quite certain that the few best will always be at the top, since the effect of random events, or luck, is so small. However, it is not certain that the absolutely best will win. Since the best can be very close to another in their skillfulness, the outcome is dominated by luck, by the small variations during the event and their effect on personal performance. Therefore, the top three or top five may consist of the same individuals from one event to another, yet the winner may never be the one with the most skill. This is called the paradox of skill. A further implication is that it is not always possible to find the best or most skillful, only the winner of a single event. If we cannot remove the effect of luck completely, small random effects make it difficult to distinguish the differences between individuals, when they are nearly equally skilled. The table below illustrates the paradox of skill. Player 1 is the most skilled, but due to small differences in skill, the least skilled Player 3 wins the game.

If an event is dominated by luck, it expresses the statistical property known as mean reversion: after an extreme event the next event is likely to be closer to the mean. This implies that extremely high or low values are often followed by mediocre results, which are in general more likely to take place. This also implies that if well above average performance does not regress to the mean or does so very slowly, an event is skill dominant.

To understand how regression to the mean can take place within groups while the variance of results in the whole population (consisting of those groups) stays the same, I encourage you to read pages 16 – 19 in Mr. Mauboussin’s paper.

Guidance to prevent us from being mislead

Not seldom are we inclined to draw conclusions from too small samples. For example, if an event is dominated by luck, we might nevertheless attribute the success of individuals to higher than average skill, although the outcome was dominated by luck. Therefore it is important to distinguish between skill and luck dominant events and also ascertain, what kind of tests are required to reach a solid conclusion.

Fair appraisal and evaluation and promoting learning

The roles that skill and luck play determine fair assessment and appraisal. If we are praised or punished for events that occur due to luck, the consequences are unlikely to be seen fair, eventually even morally wrong. An interesting point is that in some cases people are conditioned to being rewarded for giving bad feedback and going unrewarded for giving good feedback. This leads to restraining from praising a good performance, be it due to skills or luck, and calling errors in to attention, be they due to skill or luck. See Daniel Kahneman’s observation in Wikipedia for more information.

Mauboussin states in his paper, quite right in my opinion, that when luck is dominating an event, we should praise people for sticking to a prescribed process and becoming more proficient in the process and its execution. Only this way do we learn, by deliberately sticking to a process, repeating it and honing it. That is a time consuming process and not always pleasant or easy. However, this repetition improves our skills and reduces the role of luck in the considered event, making it more skill based. Therefore we should, as the cliché often goes, praise and give prize for hard work and effort, not necessarily for the good outcome.

Optimal resource allocation

If we know an event to be dominated by skill, we can improve the outcome by allocating more to the disposal of the most skillful ones, assuming the they still have excess capacity to use those resources. For example, two factories produce similar goods, but the other one has a more advanced technology that leads to lower production costs. However, this factory does not have enough capital to invest into more production capacity that is needed in any case to produce all the demanded goods. We might reduce the funding (bank loans for investments into new equipment) to the other factory and use it to increase the capacity in the more efficient one, thus reducing total average production costs while increasing capacity. If both factories would be efficient one day and completely inefficient the other, production thus being a luck dominated effect, such re-allocation of capital would not make sense.

Like in the case of randomly efficient factories, we should not take a bank loan and ask the last week’s lottery winner to play with the whole sum. As lottery is a pure game of luck, investing in a specific individual does not increase the chance of success.

Edit 10.9.2016: Corrected typos.

## Use your tools, lest they rust away

During the Coursera course on model thinking I have learned the basic principles of many models, ranging from segregation over economical models to military strategy. I have learned how simple can models can give insights into many everyday problems and phenomena, when you know the correct models and can apply them. And that is the difficult part.

Like with any tools, models are only useful when they are used at the right time for the right purpose, and in order to do that they have to be available, applicable and the user has to be proficient in using them. It’s not unlike with kitchen knives. They are only useful when you have the correct knife available, it is sharp and you are proficient in using it.

In order to improve my modeling abilities and use my newly acquired toolset I will try to ask myself more often, which model I could use to analyze a problem or a phenomenon. Currently the newspapers are almost daily offering reports about the refugee crisis in the Middle-East and Europe. I could imagine doing some simple analyses using the segregation model to analyze, when and why domestic people might move inside a country when refugees arrive from abroad. Or I might use a simple demand and supply model, combined with an oligopolistic market model to analyze what the effect of the current oil price on the Russian economy will be in the long term, if the price even will stay low for an extended period of time.

Now I have my set of knives before me. It’s up to me to learn how to use them, keep them sharp and wield them when they can help me slice a problem into more digestible pieces.

## The spring is coming

In a previous blog post from two weeks ago I was happy about the winter having finally arrived to us. In the same post I mentioned the possibility of an early spring, meaning a very short winter. Although I am no expert on meteorology or weather forecasting on the short or long term, I have grown to appreciate how the seasons can start or end much earlier than one would normally expect. With short winter I was thinking anything between a few weeks until the end of February. This time it might be that the two week winter season is all we’ll be having around my neighborhood.

Already last week, in January, we had a couple of sunny days, reminding me of March. Today we had yet another sunny and relatively warm day. In the sun I was very comfortable, wearing only a thin wind stopper. After the sun had set it got cooler, but I for me that is also a characteristic of the first spring days and weeks. I am eager to see whether the spring already starts to advance or if it will for once more be overcome and delayed by the winter, even for a short while.