http://en.wikipedia.org/wiki/Karelian_Fortified_Region
http://en.wikipedia.org/wiki/Adolf_Hitler
Adolf Hitler (German: [ˈadɔlf ˈhɪtlɐ] ( listen); 20 April 1889 – 30 April 1945) was an Austrian-born German politician and the leader of the Nazi Party (German: Nationalsozialistische Deutsche Arbeiterpartei (NSDAP); National Socialist German Workers Party). He was chancellor of Germany from 1933 to 1945 and dictator of Nazi Germany (as Führer und Reichskanzler) from 1934 to 1945. He was at the centre of the founding of Nazism, World War II, and the Holocaust.
A decorated veteran of World War I, Hitler joined the German Workers' Party (precursor of the NSDAP) in 1919, and became leader of the NSDAP in 1921. In 1923, he attempted a coup d'état in Munich, known as the Beer Hall Putsch. The failed coup resulted in Hitler's imprisonment, during which time he wrote his memoir, Mein Kampf (My Struggle). After his release in 1924, Hitler gained popular support by attacking the Treaty of Versailles and promoting Pan-Germanism, antisemitism, and anti-communism with charismatic oratory and Nazi propaganda. After his appointment as chancellor in 1933, he transformed the Weimar Republic into the Third Reich, a single-party dictatorship based on the totalitarian and autocratic ideology of Nazism.
Hitler's aim was to establish a New Order of absolute Nazi German hegemony in continental Europe. To this end, his foreign and domestic policies sought to seize Lebensraum ("living space") for the Germanic people. He directed the rearmament of Germany and the invasion of Poland by the Wehrmacht in September 1939, resulting in the outbreak of World War II in Europe. Under Hitler's rule, in 1941 German forces and their European allies occupied most of Europe and North Africa. By 1943, Hitler's military decisions led to escalating defeats. In 1945 the Allied armies successfully invaded Germany. Hitler's supremacist and racially motivated policies resulted in the systematic murder of eleven million people, including an estimated six million Jews, and in the deaths of between 50 and 70 million people in World War II.
In the final days of the war, during the Battle of Berlin in 1945, Hitler married his long-time mistress, Eva Braun. On 30 April 1945, less than two days later, the two committed suicide to avoid capture by the Red Army, and their corpses were burned.
http://en.wikipedia.org/wiki/Siege_of_Leningrad
The Siege of Leningrad, also known as the Leningrad Blockade (Russian: блокада Ленинграда, transliteration: blokada Leningrada) was a prolonged military operation undertaken by the German Army Group North against Leningrad—historically and currently known as Saint Petersburg—in the Eastern Front
theatre of World War II. The siege started on 8 September 1941, when
the last land connection to the city was severed. Although the Soviets
managed to open a narrow land corridor to the city on 18 January 1943,
lifting of the siege
took place on 27 January 1944, 872 days after it began. It was one of
the longest and most destructive sieges in history and overwhelmingly
the most costly in terms of casualties.[7]
http://en.wikipedia.org/wiki/Operation_Barbarossa
Operation Barbarossa, or Case Barbarossa, was the code name for Germany's invasion of the Soviet Union during the Second World War.[14][15] Beginning on 22 June 1941, over 4 million troops of the Axis powers invaded the USSR along a 2,900 km (1,800 mi) front,[16]
the largest invasion in the history of warfare. In addition to troops,
Barbarossa involved 600,000 motor vehicles and 750,000 horses.[17] The ambitious operation, driven by Adolf Hitler's
persistent desire to conquer the Russian territories, marked the
beginning of the pivotal phase in deciding the victors of the war. The
German invasion of the Soviet Union ultimately resulted in 95% of all
German Army casualties from 1941 to 1944 and 65% of all Allied military
casualties accumulated throughout the war.
Operation Barbarossa was named after Frederick Barbarossa,
the medieval Holy Roman Emperor. Planning started on 18 December 1940;
the secret preparations and the military operation itself lasted from
June to December 1941. The Red Army repelled the Wehrmacht's strongest blow, and Adolf Hitler
did not achieve the expected victory, but the Soviet Union's situation
remained dire. Tactically, the Germans won resounding victories and
occupied some of the most important economic areas of the Soviet Union,
mainly in Ukraine.[18] Despite these successes, the Germans were pushed back from Moscow and could never again mount a simultaneous offensive along the entire strategic Soviet–German front.[19]
Operation Barbarossa's failure led to Hitler's demands for further
operations inside the USSR, all of which eventually failed, such as
continuing the Siege of Leningrad,[20][21] Operation Nordlicht, and Battle of Stalingrad, among other battles on occupied Soviet territory.[22][23][24][25][26]
Operation Barbarossa was the largest military operation in human history in both manpower and casualties.[27] Its failure was a turning point in the Third Reich's fortunes. Most importantly, Operation Barbarossa opened up the Eastern Front,
to which more forces were committed than in any other theater of war in
world history. Regions covered by the operation became the site of some
of the largest battles[which?], deadliest atrocities[which?],
highest casualties, and most horrific conditions for Soviets and
Germans alike — all of which influenced the course of both World War II
and 20th century history. The German forces captured 3 million Soviet POWs, who did not enjoy the protection stipulated in the Geneva Conventions.[28] Most of them never returned alive.[29] They were deliberately starved to death in German camps as part of a Hunger Plan, i.e., the program to reduce the Eastern European population.[30]
http://en.wikipedia.org/wiki/Nazi_Germany
Nazi Germany, also known as the Third Reich, is the common name for Germany when it was a totalitarian state ruled by Adolf Hitler and his National Socialist German Workers' Party (NSDAP). On 30 January 1933 Hitler became Chancellor of Germany, quickly eliminating all opposition to rule as sole leader. The state idolized Hitler as its Führer
("leader"), centralizing all power in his hands. Historians have
emphasized the hypnotic effect of his rhetoric on large audiences, and
of his eyes in small groups. Kessel writes, "Overwhelmingly...Germans
speak with mystification of Hitler's 'hypnotic' appeal..."[4]
Under the "leader principle", the Führer's word was above all other
laws. Top officials reported to Hitler and followed his policies, but
they had considerable autonomy. The government was not a coordinated,
cooperating body, but rather a collection of factions struggling to
amass power and gain favor with the Führer.[5] In the midst of the Great Depression, the Nazi government restored economic stability and ended mass unemployment using heavy military spending and a mixed economy of free-market and central-planning practices.[6] Extensive public works were undertaken, including the construction of the Autobahns.
The return to economic stability gave the regime enormous popularity;
the suppression of all opposition made Hitler's rule mostly
unchallenged.
Racism, especially antisemitism, was a main tenet of society in Nazi Germany. The Gestapo (secret state police) and SS under Heinrich Himmler
destroyed the liberal, socialist, and communist opposition, and
persecuted and murdered Jews and other "undesirables". It was believed
that the Germanic peoples—who were also referred to as the Nordic race—were the purest representation of the Aryan race, and were therefore the master race. Education focused on racial biology, population policy, and physical fitness. Membership in the Hitler Youth
organization became compulsory. The number of women enrolled in
post-secondary education plummeted, and career opportunities were
curtailed. Calling women's rights a "product of the Jewish intellect,"
the Nazis practiced what they called "emancipation from emancipation."[7] Entertainment and tourism were organized via the Strength Through Joy
program. The government controlled artistic expression, promoting
specific forms of art and discouraging or banning others. The Nazis
mounted the infamous Entartete Kunst (Degenerate Art) exhibition in 1937.[8] Propaganda minister Joseph Goebbels made effective use of film, mass rallies, and Hitler's hypnotizing oratory to control public opinion.[9] The 1936 Summer Olympics showcased the Third Reich on the international stage.
Germany made increasingly aggressive demands, threatening war if they were not met. Britain and France responded with appeasement, hoping Hitler would finally be satisfied.[10] Austria was annexed in 1938, and the Sudetenland was taken via the Munich Agreement in 1938, with the rest of Czechoslovakia taken over in 1939. Hitler made a pact with Joseph Stalin and invaded Poland in September 1939, starting World War II. In alliance with Benito Mussolini's Italy,
Germany conquered France and most of Europe by 1940, and threatened its
remaining major foe: Great Britain. Reich Commissariats took brutal
control of conquered areas, and a German administration termed the General Government was established in Poland. Concentration camps,
established as early as 1933, were used to hold political prisoners and
opponents of the regime. The number of camps quadrupled between 1939
and 1942 to 300+, as slave-laborers from across Europe, Jews, political
prisoners, criminals, homosexuals, gypsies, the mentally ill and others
were imprisoned. The system that began as an instrument of political
oppression culminated in the mass genocide of Jews and other minorities in The Holocaust.
Following the German invasion of the Soviet Union in 1941, the tide turned against the Third Reich in the major military defeats of the Battle of Stalingrad and the Battle of Kursk
in 1943. The Soviet counter-attacks became the largest land battles in
history. Large-scale systematic bombing of all major German cities, rail
lines and oil plants escalated in 1944, shutting down the Luftwaffe
(German Air Force). Germany was overrun in 1945 by the Soviets from the
east and the Allies from the west. The victorious Allies initiated a policy of denazification and put the surviving Nazi leadership on trial for war crimes at the Nuremberg Trials.
http://en.wikipedia.org/wiki/Battle_of_Stalingrad
The Battle of Stalingrad was a major and decisive battle of World War II in which Nazi Germany and its allies fought the Soviet Union for control of the city of Stalingrad (now Volgograd) in the southwestern Soviet Union. The battle took place between 23 August 1942 and 2 February 1943[6][7][8][9] and was marked by constant close-quarters combat and lack of regard for military and civilian casualties. It is among the bloodiest battles in the history of warfare,
with the higher estimates of combined casualties amounting to nearly
two million. The heavy losses inflicted on the German army made it a
significant turning point in the whole war.[10]
After the Battle of Stalingrad, German forces never recovered their
earlier strength, and attained no further strategic victories in the
East.[11]
The German offensive to capture Stalingrad commenced in late summer 1942, and was supported by intensive Luftwaffe bombing that reduced much of the city to rubble. The German offensive eventually became mired in building-to-building fighting; and despite controlling nearly all of the city at times, the Wehrmacht was unable to dislodge the last Soviet defenders clinging tenaciously to the west bank of the Volga River.
On 19 November 1942, the Red Army launched Operation Uranus, a two-pronged attack targeting the weaker Romanian and Hungarian forces protecting the 6th Army's flanks.[12] After heavy fighting, the weakly held Axis flanks collapsed and the 6th Army was cut off and surrounded
inside Stalingrad. As the Russian winter set in, the 6th Army weakened
rapidly from cold, starvation and ongoing Soviet attacks. Command
ambiguity coupled with Adolf Hitler's resolute belief in their will to
fight further exacerbated the German predicament. Eventually, the
failure of outside German forces to break the encirclement, coupled with
the failure of resupplying by air, led to the final collapse. By the
beginning of February 1943, Axis resistance in Stalingrad had ceased and
the remaining elements of the 6th Army had either surrendered or been
destroyed.[13]:p.932
MEN'S WISDOM NOW
Sunday, December 9, 2012
Friday, November 9, 2012
Model Thinking - michigan university coursea
This course will consist of twenty sections. As the course proceeds,
I will fill in the descriptions of the topics and put in readings.
Section 1: Introduction: Why Model? In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories:
The Model Thinker: Prologue, Introduction and Chapter 1
Why Model? by Joshua Epstein
Section 2: Sorting and Peer Effects We now jump directly into some models. We contrast two types of models that explain a single phenomenon, namely that people tend to live and interact with people who look, think, and act like themselves. After an introductory lecture, we cover famous models by Schelling and Granovetter that cover these phenomena. We follows those with a fun model about standing ovations that I wrote with my friend John Miller.
In this second section, I show a computational version of Schelling's Segregation Model using NetLogo. Netlogo is free software authored by Uri Wilensky or Northwestern University. I will be using NetLogo several times during the course. It can be downloaded here:
NetLogo
The Schelling Model that I use can be found by clicking on the "File" tab, then going to "Models Library". In the Models Library directory, click on the arrow next to the Social Science folder and then scroll down and click on the model called Segregation.
The readings for this section include some brief notes on Schelling's model and then the academic papers of Granovetter and Miller and Page. I'm not expecting you to read those papers from start to end, but I strongly encourage you to peruse them so that you can see how social scientists frame and interpret models.
Notes on Schelling
Granovetter Model
Miller and Page Model
Section 3: Aggregation In this section, we explore the mysteries of aggregation, i.e. adding things up. We start by considering how numbers aggregate, focusing on the Central Limit Theorem. We then turn to adding up rules. We consider the Game of Life and one dimensional cellular automata models. Both models show how simple rules can combine to produce interesting phenomena. Last, we consider aggregating preferences. Here we see how individual preferences can be rational, but the aggregates need not be.
There exist many great places on the web to read more about the Central Limit Theorem, the Binomial Distribution, Six Sigma, The Game of LIfe, and so on. I've included some links to get you started. The readings for cellular automata and for diverse preferences are short excerpts from my books Complex Adaptive Social Systems and The Difference Respectively.
Central Limit Theorem
Binomial Distribution
Six Sigma
Cellular Automata1
Cellular Automata2
Diverse Preferences
Section 4: Decision Models In this section, we study some models of how people make decisions. We start by considering multi criterion decision making. We then turn to spatial models of decision making and then decision trees. We conclude by looking at the value of information..
The reading for multi-criterion decision making will be my guide for the Michigan Civil Rights Initiative. It provides a case study for how to use this technique. For spatial voting and decision models, there exist many great power point presentations and papers on the web. The Decision Tree writings are from Arizona State University's Craig Kirkwood.
Multi Criterion Decision Making Case Study
Spatial Models
Decision Theory
Section 5: Models of People: Thinking Electrons In this section, we study various ways that social scientists model people. We study and contrast three different models. The rational actor approach, behavioral models , and rule based models . These lectures provide context for many of the models that follow. There's no specific reading for these lectures though I mention several books on behavioral economics that you may want to consider. Also, if you find the race to the bottom game interesting just type "Rosemary Nagel Race to the Bottom" into a search engine and you'll get several good links. You can also find good introductions to "Zero Intelligence Traders" by typing that in as well.
Here is a link to a brief primer on behavioral economics that has more references.
Short Primer on Behavioral Economics
Section 6: Linear Models In this section, we cover linear models. We start by looking at categorical models, in which data gets binned into categories. We use this simple framework to introduce measures like mean, variance, and R-squared. We then turn to linear models describing what linear models do, how to read regression output (a valuable skill!) and how to fit nonlinear data with linear models. These lectures are meant to give you a "feel" for how linear models are used and perhaps to motivate you to take a course on these topics. I conclude this section by highlighting a distinction between what I call Big Coefficient thinking and New Reality thinking. The readings for this section consist of two short pieces written by me, but you can find abundant resources on the web on linear models, R-squared, regression, and evidence based thinking.
Categorical Models
Linear Models
Section 7: Tipping Points In this section, we cover tipping points. We focus on two models. A percolation model from physics that we apply to banks and a model of the spread of diseases. The disease model is more complicated so I break that into two parts. The first part focuses on the diffusion. The second part adds recovery. The readings for this section consist of two excerpts from the book I'm writing on models. One covers diffusion. The other covers tips. There is also a technical paper on tipping points that I've included in a link. I wrote it with PJ Lamberson and it will be published in the Quarterly Journal of Political Science. I've included this to provide you a glimpse of what technical social science papers look like. You don't need to read it in full, but I strongly recommend the introduction. It also contains a wonderful reference list.
Tipping Points
DIffusion and SIS
Lamberson and Page: Tipping Points (READ INTRO ONLY)
Section 8: Economic Growth In this section, we cover several models of growth. We start with a simple model of exponential growth and then move on to models from economics, with a focus on Solow's basic growth model. I simplify the model by leaving out the labor component. These models help us distinguish between two types of growth: growth that occurs from capital accumulation and growth that occurs from innovation.
Growth Models
Section 9: Diversity and Innovation In this section, we cover some models of problem solving to show the role that diversity plays in innovation. We see how diverse perspectives (problem representations) and heuristics enable groups of problem solvers to outperform individuals. We also introduce some new concepts like "rugged landscapes" and "local optima". In the last lecture, we'll see the awesome power of recombination and how it contributes to growth. The readings for this chapters consist on an excerpt from my book The Difference courtesy of Princeton University Press.
Diversity and Problem Solving
Section 10: Markov Processes In this section, we cover Markov Processes. Markov Processes capture dynamic processes between a fixed set of states. For example, we will consider a process in which countries transition between democratic and dictatorial. To be a Markov Process, it must be possible to get from any one state to any other and the probabilities of moving between states must remain fixed over time. If those assumptions hold, then the process will have a unique equilibrium. In other words, history will not matter. Formally, this result is called the Markov Convergence Theorem. In addition to covering Markov Processes, we will also see how the basic framework can be used in other applications such as determining authorship of a text and the efficacy of a drug protocol.
Markov Processes
Section 11: Lyapunov Functions Models can help us to determine the nature of outcomes produced by a system: will the system produce an equilibrium, a cycle, randomness, or complexity? In this set of lectures, we cover Lyapunov Functions. These are a technique that will enable us to identify many systems that go to equilibrium. In addition, they enable us to put bounds on how quickly the equilibrium will be attained. In this set of lectures, we learn the formal definition of Lyapunov Functions and see how to apply them in a variety of settings. We also see where they don't apply and even study a problem where no one knows whether or not the system goes to equilibrium or not.
Lyapunov Functions
Section 12: Coordination and Culture In this set of lectures, we consider some models of culture. We begin with some background on what culture is and why it's so important to social scientists. In the analytic section, we begin with a very simple game called the pure coordination game In this game, the players win only if they choose the same action. Which action they choose doesn't matter -- so long as they choose the same one. For example, whether you drive on the left or the right side of the road is not important, but what is important is that you drive on the same side as everyone else. We then consider situations in which people play multiple coordination games and study the emergence of culture. In our final model, we include a desire consistency as well as coordination in a model that produces the sorts of cultural signatures seen in real world data. The readings for this section include some of my notes on coordination games and then the Bednar et al academic paper. In that paper, you see how we used Markov Processes to study the model. There is also a link to the Axelrod Net Logo Model.
Coordination Games
Bednar et al. 2010
Axelrod Culture Model in Netlogo
Section 13: Path Dependence
In this set of lectures, we cover path dependence. We do so using some very simple urn models. The most famous of which is the Polya Process. These models are very simple but they enable us to unpack the logic of what makes a process path dependent. We also relate path dependence to increasing returns and to tipping points. The reading for this lecture is a paper that I wrote that is published in the Quarterly Journal of Political Science
Path Dependence
Section 14: Networks
In this section, we cover networks. We discuss how networks form, their structure -- in particular some common measures of networks -- and their function. Often, networks exhibit functions that emerge, but that we mean that no one intended for the functionality but it arises owing to the structure of the network. The reading for this section is a short article by Steven Strogatz.
Strogatz
Section 15:Randomness and Random Walks
In this section, we first discuss randomness and its various sources. We then discuss how performance can depend on skill and luck, where luck is modeled as randomness. We then learn a basic random walk model, which we apply to the Efficient Market Hypothesis, the ideas that market prices contain all relevant information so that what's left is randomness. We conclude by discussing finite memory random walk model that can be used to model competition. The reading for this section is a paper on distinguishing skill from luck by Michael Mauboussin. Mauboussin: Skill vs Luck
Section 16: The Colonel Blotto Game
In this section, we cover the Colonel Blotto Game. This game was originally developed to study war on multiple fronts. It's now applied to everything from sports to law to terrorism. We will discuss the basics of Colonel Blotto, move on to some more advanced analysis and then contrast Blotto with our skill luck model from the previous section. The readings for this section are an excerpt from my book The Difference and a paper that I wrote with Russell Golman of Carnegie Mellon. You need only read the first four pages of the Golman paper.
Blotto from The Difference
Golman Page: General Blotto
Section 17:The Prisoners' Dilemma and Collective Action
In this section, we cover the Prisoners' Dilemma, Collective Action Problems and Common Pool Resource Problems. We begin by discussion the Prisoners' Dilemma and showing how individual incentives can produce undesirable social outcomes. We then cover seven ways to produce cooperation. Five of these will be covered in the paper by Nowak and Sigmund listed below. We conclude by talking about collective action and common pool resource problems and how they require deep careful thinking to solve. There's a wonderful piece to read on this by the Nobel Prize winner Elinor Ostrom
The Prisoners' Dilemma in the Stanford Encyclopedia of Philosophy
Nowak and Sigmund: Five Ways to Cooperate
Ostrom: Going Beyond Panaceas
Section 18: Mechanism Design: Auctions
In this section, we cover mechanism design. We begin with some of the basics: how to overcome problems of hidden action and hidden information. We then turn to the more applied question of how to design auctions. We conclude by discussion how one can use mechanisms to make decisions about public projects. The readings for this section consist of a piece by the Eric Maskin who won a Nobel Prize for his work on mechanism design and some slides on auctions by V.S. Subrahmanian. The Maskin article can be tough sledding near the end. Don't worry about necessarily understanding everything. Focus on the big picture that he describes.
Maskin: Mechanism Design
V.S. Subrahmanian's auction slides
Section 19: Learning: Replicator Dynamics
In this section, we cover replicator dynamics and Fisher's fundamental theorem. Replicator dynamics have been used to explain learning as well as evolution. Fisher's theorem demonstrates how the rate of adaptation increases with the amount of variation. We conclude by describing how to make sense of both Fisher's theorem and our results on six sigma and variation reduction. The readings for this section are very short. The second reading on Fisher's theorem is rather technical. Both are excerpts from Diversity and Complexity
The Replicator Equation
Fisher's Theorem
Section 20: The Many Model Thinker: Diversity and Prediction
In our final section, we cover the value of ability and diversity to create wise crowds when making predictions. We start off by talking about category models and linear models and how they can be used to make predictions. We then cover the Diversity Prediction Theorem, which provides basic intuition for how collective prediction works. We conclude by talking about the value of having lots of models. The reading for this section is a short explanation of the diversity prediction theorem.
Diversity Prediction Theorem
Section 1: Introduction: Why Model? In these lectures, I describe some of the reasons why a person would want to take a modeling course. These reasons fall into four broad categories:
- To be an intelligent citizen of the world
- To be a clearer thinker
- To understand and use data
- To better decide, strategize, and design
The Model Thinker: Prologue, Introduction and Chapter 1
Why Model? by Joshua Epstein
Section 2: Sorting and Peer Effects We now jump directly into some models. We contrast two types of models that explain a single phenomenon, namely that people tend to live and interact with people who look, think, and act like themselves. After an introductory lecture, we cover famous models by Schelling and Granovetter that cover these phenomena. We follows those with a fun model about standing ovations that I wrote with my friend John Miller.
In this second section, I show a computational version of Schelling's Segregation Model using NetLogo. Netlogo is free software authored by Uri Wilensky or Northwestern University. I will be using NetLogo several times during the course. It can be downloaded here:
NetLogo
The Schelling Model that I use can be found by clicking on the "File" tab, then going to "Models Library". In the Models Library directory, click on the arrow next to the Social Science folder and then scroll down and click on the model called Segregation.
The readings for this section include some brief notes on Schelling's model and then the academic papers of Granovetter and Miller and Page. I'm not expecting you to read those papers from start to end, but I strongly encourage you to peruse them so that you can see how social scientists frame and interpret models.
Notes on Schelling
Granovetter Model
Miller and Page Model
Section 3: Aggregation In this section, we explore the mysteries of aggregation, i.e. adding things up. We start by considering how numbers aggregate, focusing on the Central Limit Theorem. We then turn to adding up rules. We consider the Game of Life and one dimensional cellular automata models. Both models show how simple rules can combine to produce interesting phenomena. Last, we consider aggregating preferences. Here we see how individual preferences can be rational, but the aggregates need not be.
There exist many great places on the web to read more about the Central Limit Theorem, the Binomial Distribution, Six Sigma, The Game of LIfe, and so on. I've included some links to get you started. The readings for cellular automata and for diverse preferences are short excerpts from my books Complex Adaptive Social Systems and The Difference Respectively.
Central Limit Theorem
Binomial Distribution
Six Sigma
Cellular Automata1
Cellular Automata2
Diverse Preferences
Section 4: Decision Models In this section, we study some models of how people make decisions. We start by considering multi criterion decision making. We then turn to spatial models of decision making and then decision trees. We conclude by looking at the value of information..
The reading for multi-criterion decision making will be my guide for the Michigan Civil Rights Initiative. It provides a case study for how to use this technique. For spatial voting and decision models, there exist many great power point presentations and papers on the web. The Decision Tree writings are from Arizona State University's Craig Kirkwood.
Multi Criterion Decision Making Case Study
Spatial Models
Decision Theory
Section 5: Models of People: Thinking Electrons In this section, we study various ways that social scientists model people. We study and contrast three different models. The rational actor approach, behavioral models , and rule based models . These lectures provide context for many of the models that follow. There's no specific reading for these lectures though I mention several books on behavioral economics that you may want to consider. Also, if you find the race to the bottom game interesting just type "Rosemary Nagel Race to the Bottom" into a search engine and you'll get several good links. You can also find good introductions to "Zero Intelligence Traders" by typing that in as well.
Here is a link to a brief primer on behavioral economics that has more references.
Short Primer on Behavioral Economics
Section 6: Linear Models In this section, we cover linear models. We start by looking at categorical models, in which data gets binned into categories. We use this simple framework to introduce measures like mean, variance, and R-squared. We then turn to linear models describing what linear models do, how to read regression output (a valuable skill!) and how to fit nonlinear data with linear models. These lectures are meant to give you a "feel" for how linear models are used and perhaps to motivate you to take a course on these topics. I conclude this section by highlighting a distinction between what I call Big Coefficient thinking and New Reality thinking. The readings for this section consist of two short pieces written by me, but you can find abundant resources on the web on linear models, R-squared, regression, and evidence based thinking.
Categorical Models
Linear Models
Section 7: Tipping Points In this section, we cover tipping points. We focus on two models. A percolation model from physics that we apply to banks and a model of the spread of diseases. The disease model is more complicated so I break that into two parts. The first part focuses on the diffusion. The second part adds recovery. The readings for this section consist of two excerpts from the book I'm writing on models. One covers diffusion. The other covers tips. There is also a technical paper on tipping points that I've included in a link. I wrote it with PJ Lamberson and it will be published in the Quarterly Journal of Political Science. I've included this to provide you a glimpse of what technical social science papers look like. You don't need to read it in full, but I strongly recommend the introduction. It also contains a wonderful reference list.
Tipping Points
DIffusion and SIS
Lamberson and Page: Tipping Points (READ INTRO ONLY)
Section 8: Economic Growth In this section, we cover several models of growth. We start with a simple model of exponential growth and then move on to models from economics, with a focus on Solow's basic growth model. I simplify the model by leaving out the labor component. These models help us distinguish between two types of growth: growth that occurs from capital accumulation and growth that occurs from innovation.
Growth Models
Section 9: Diversity and Innovation In this section, we cover some models of problem solving to show the role that diversity plays in innovation. We see how diverse perspectives (problem representations) and heuristics enable groups of problem solvers to outperform individuals. We also introduce some new concepts like "rugged landscapes" and "local optima". In the last lecture, we'll see the awesome power of recombination and how it contributes to growth. The readings for this chapters consist on an excerpt from my book The Difference courtesy of Princeton University Press.
Diversity and Problem Solving
Section 10: Markov Processes In this section, we cover Markov Processes. Markov Processes capture dynamic processes between a fixed set of states. For example, we will consider a process in which countries transition between democratic and dictatorial. To be a Markov Process, it must be possible to get from any one state to any other and the probabilities of moving between states must remain fixed over time. If those assumptions hold, then the process will have a unique equilibrium. In other words, history will not matter. Formally, this result is called the Markov Convergence Theorem. In addition to covering Markov Processes, we will also see how the basic framework can be used in other applications such as determining authorship of a text and the efficacy of a drug protocol.
Markov Processes
Section 11: Lyapunov Functions Models can help us to determine the nature of outcomes produced by a system: will the system produce an equilibrium, a cycle, randomness, or complexity? In this set of lectures, we cover Lyapunov Functions. These are a technique that will enable us to identify many systems that go to equilibrium. In addition, they enable us to put bounds on how quickly the equilibrium will be attained. In this set of lectures, we learn the formal definition of Lyapunov Functions and see how to apply them in a variety of settings. We also see where they don't apply and even study a problem where no one knows whether or not the system goes to equilibrium or not.
Lyapunov Functions
Section 12: Coordination and Culture In this set of lectures, we consider some models of culture. We begin with some background on what culture is and why it's so important to social scientists. In the analytic section, we begin with a very simple game called the pure coordination game In this game, the players win only if they choose the same action. Which action they choose doesn't matter -- so long as they choose the same one. For example, whether you drive on the left or the right side of the road is not important, but what is important is that you drive on the same side as everyone else. We then consider situations in which people play multiple coordination games and study the emergence of culture. In our final model, we include a desire consistency as well as coordination in a model that produces the sorts of cultural signatures seen in real world data. The readings for this section include some of my notes on coordination games and then the Bednar et al academic paper. In that paper, you see how we used Markov Processes to study the model. There is also a link to the Axelrod Net Logo Model.
Coordination Games
Bednar et al. 2010
Axelrod Culture Model in Netlogo
Section 13: Path Dependence
In this set of lectures, we cover path dependence. We do so using some very simple urn models. The most famous of which is the Polya Process. These models are very simple but they enable us to unpack the logic of what makes a process path dependent. We also relate path dependence to increasing returns and to tipping points. The reading for this lecture is a paper that I wrote that is published in the Quarterly Journal of Political Science
Path Dependence
Section 14: Networks
In this section, we cover networks. We discuss how networks form, their structure -- in particular some common measures of networks -- and their function. Often, networks exhibit functions that emerge, but that we mean that no one intended for the functionality but it arises owing to the structure of the network. The reading for this section is a short article by Steven Strogatz.
Strogatz
Section 15:Randomness and Random Walks
In this section, we first discuss randomness and its various sources. We then discuss how performance can depend on skill and luck, where luck is modeled as randomness. We then learn a basic random walk model, which we apply to the Efficient Market Hypothesis, the ideas that market prices contain all relevant information so that what's left is randomness. We conclude by discussing finite memory random walk model that can be used to model competition. The reading for this section is a paper on distinguishing skill from luck by Michael Mauboussin. Mauboussin: Skill vs Luck
Section 16: The Colonel Blotto Game
In this section, we cover the Colonel Blotto Game. This game was originally developed to study war on multiple fronts. It's now applied to everything from sports to law to terrorism. We will discuss the basics of Colonel Blotto, move on to some more advanced analysis and then contrast Blotto with our skill luck model from the previous section. The readings for this section are an excerpt from my book The Difference and a paper that I wrote with Russell Golman of Carnegie Mellon. You need only read the first four pages of the Golman paper.
Blotto from The Difference
Golman Page: General Blotto
Section 17:The Prisoners' Dilemma and Collective Action
In this section, we cover the Prisoners' Dilemma, Collective Action Problems and Common Pool Resource Problems. We begin by discussion the Prisoners' Dilemma and showing how individual incentives can produce undesirable social outcomes. We then cover seven ways to produce cooperation. Five of these will be covered in the paper by Nowak and Sigmund listed below. We conclude by talking about collective action and common pool resource problems and how they require deep careful thinking to solve. There's a wonderful piece to read on this by the Nobel Prize winner Elinor Ostrom
The Prisoners' Dilemma in the Stanford Encyclopedia of Philosophy
Nowak and Sigmund: Five Ways to Cooperate
Ostrom: Going Beyond Panaceas
Section 18: Mechanism Design: Auctions
In this section, we cover mechanism design. We begin with some of the basics: how to overcome problems of hidden action and hidden information. We then turn to the more applied question of how to design auctions. We conclude by discussion how one can use mechanisms to make decisions about public projects. The readings for this section consist of a piece by the Eric Maskin who won a Nobel Prize for his work on mechanism design and some slides on auctions by V.S. Subrahmanian. The Maskin article can be tough sledding near the end. Don't worry about necessarily understanding everything. Focus on the big picture that he describes.
Maskin: Mechanism Design
V.S. Subrahmanian's auction slides
Section 19: Learning: Replicator Dynamics
In this section, we cover replicator dynamics and Fisher's fundamental theorem. Replicator dynamics have been used to explain learning as well as evolution. Fisher's theorem demonstrates how the rate of adaptation increases with the amount of variation. We conclude by describing how to make sense of both Fisher's theorem and our results on six sigma and variation reduction. The readings for this section are very short. The second reading on Fisher's theorem is rather technical. Both are excerpts from Diversity and Complexity
The Replicator Equation
Fisher's Theorem
Section 20: The Many Model Thinker: Diversity and Prediction
In our final section, we cover the value of ability and diversity to create wise crowds when making predictions. We start off by talking about category models and linear models and how they can be used to make predictions. We then cover the Diversity Prediction Theorem, which provides basic intuition for how collective prediction works. We conclude by talking about the value of having lots of models. The reading for this section is a short explanation of the diversity prediction theorem.
Diversity Prediction Theorem
Model Thinking - Michigan University coursea
Introduction: Why Model? (Section 1)
- Completed Why Model? (8:53)
- Completed Intelligent Citizens of the World (11:31)
- Completed Thinking More Clearly (10:50)
- Completed Using and Understanding Data (10:14)
- Completed
Using Models to Decide, Strategize, and Design (15:26
Segregation and Peer Effects (Section 2)
- Completed Sorting and Peer Effects Introduction (5:11)
- Completed Schelling's Segregation Model (11:30)
- Completed Measuring Segregation (11:30)
- Completed Peer Effects (6:58)
- Completed The Standing Ovation Model (18:05)
- Completed The Identification Problem (10:18)
Aggregation (Section 3)
- Completed Aggregation (10:15)
- Completed Central Limit Theorem (18:52)
- Completed Six Sigma (5:11)
- Completed Game of Life (14:36)
- Completed Cellular Automata (18:07)
- Completed Preference Aggregation (12:19)
Decision Models (Section 4)
- Completed Introduction to Decision Making (5:37)
- Completed Multi-Criterion Decision Making (8:18)
- Completed Spatial Choice Models (11:08)
- Completed Probability: The Basics (10:06)
- Completed Decision Trees (14:38)
- Completed Value of Information (8:41)
Thinking Electrons: Modeling People (Section 5)
- Completed Thinking Electrons: Modeling People (6:29)
- Completed Rational Actor Models (16:09)
- Completed Behavioral Models (12:49)
- Completed Rule Based Models (12:30)
- Completed When Does Behavior Matter? (12:40)
Categorical and Linear Models (Section 6)
- Completed Introduction to Linear Models (4:27)
- Completed Categorical Models (15:13)
- Completed Linear Models (8:10)
- Completed Fitting Lines to Data (11:48)
- Completed Reading Regression Output (11:44)
- Completed From Linear to Nonlinear (6:11)
- Completed The Big Coefficient vs The New Reality (11:26)
Tipping Points (Section 7)
- Completed Tipping Points (5:58)
- Completed Percolation Models (11:48)
- Completed Contagion Models 1: Diffusion (7:24)
- Completed Contagion Models 2: SIS Model (9:12)
- Completed Classifying Tipping Points (8:26
- Completed Measuring Tips (13:39)
Economic Growth (Section 8)
(expanded, click to collapse)- Completed Introduction To Growth (6:43)
- Completed Exponential Growth (10:53)
- Completed Basic Growth Model (13:59)
- Completed Solow Growth Model (11:41)
- Completed WIll China Continue to Grow? (11:55)
- Completed Why Do Some Countries Not Grow? (11:30)
Diversity and Innovation (Section 9)
- Completed Problem Solving and Innovation (5:06)
- Completed Perspectives and Innovation (17:22)
- Completed Heuristics (9:29)
- Completed Teams and Problem Solving (11:05)
- Completed Recombination (11:02)
Markov Processes (Section 10)
- Completed Markov Models (4:26)
- Completed A Simple Markov Model (11:27)
- Completed Markov Model of Democratization (8:21)
- Completed Exapting the Markov Model (10:11)
Lyapunov Functions (Section 11)
- Completed Lyapunov Functions (9:13)
- Completed The Organization of Cities (12:14)
- Completed Exchange Economies and Externalities (9:18)
- Completed Time to Convergence and Optimality (8:04)
- Completed Lyapunov: Fun and Deep (8:40)
- Completed Lyapunov or Markov (7:24)
Coordination and Culture (Section 12)
- Completed Coordination and Culture (3:37)
- Completed What Is Culture And Why Do We Care? (15:43)
- Completed Pure Coordination Game (13:48)
- Completed Emergence of Culture (11:01)
- Completed Coordination and Consistency (17:03)
Path Dependence (Section 13)
- Completed Path Dependence (7:23)
- Completed Urn Models (16:26)
- Completed Mathematics on Urn Models (14:46)
- Completed Path Dependence and Chaos (11:08)
- Completed Path Dependence and Increasing Returns (12:31)
- Completed Path Dependent or Tipping Point (9:52)
Networks (Section 14)
- Completed Networks (7:04)
- Completed The Structure of Networks (19:30)
- Completed Network Function (13:10)
Randomness and Random Walks (Section 15
- Completed Randomness and Random Walk Models (3:05)
- Completed Sources of Randomness (5:15)
- Completed Skill and Luck (8:28)
- Completed Random Walks (12:29)
- Completed Random Walks and Wall Street (7:51)
- Completed FInite Memory Random Walks (8:18)
Colonel Blotto (Section 16)
- Completed Colonel Blotto Game (1:53)
- Completed Blotto: No Best Strategy (7:27)
- Completed Applications of Colonel Blotto (7:08)
- Completed Blotto: Troop Advantages (6:27)
- Completed Blotto and Competition (10:41)
Prisoners' Dilemma and Collective Action (Section 17)
- Completed Intro: The Prisoners' Dilemma and Collective Action (3:44)
- Completed The Prisoners' Dilemma Game (13:45)
- Completed Seven Ways To Cooperation (15:20)
- Completed Collective Action and Common Pool Resource Problems (7:23)
- Completed No Panacea (6:03)
Mechanism Design (Section 18)
- Completed Mechanism Design (4:00)
- Completed Hidden Action and Hidden Information (9:53)
- Completed Auctions (19:59)
- Public Projects (12:21)
Learning Models: Replicator Dynamics (Section 19)
- Replicator Dynamics (4:37)
- The Replicator Equation (13:29)
- Fisher's Theorem (11:57)
- Variation or Six Sigma (5:39)
Prediction and the Many Model Thinker (Section 20)
No Free Lunch Theorem:
No Free Lunch Theorem: "states that any two optimization algorithms are equivalent when their performance is averaged across all possible problems."
i.e. any heuristic is the same as any other when averaged across infinite number of cases.
Definitions:
Six Sigma: Six Sigma seeks to improve the quality of process outputs by minimizing variability in manufacturing and business processes, and identifying and removing the causes of defects (errors). (reduce variability to increase production quality)
Fisher's fundamental theorem: The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time. (More variance = better at adapting)
When use them??
Use Six Sigma to reduce variation when rugged landscape is fixed.
--> Concentrate when not much in environment changes.
Use Fisher's fundamental theorem when rugged landscape moves.
More variation allows for more chance of adpation.
--> Variance when environment has changed a lot.
---
http://en.wikipedia.org/wiki/Adaptive_landscapes
http://en.wikipedia.org/wiki/Hill_climbing
i.e. any heuristic is the same as any other when averaged across infinite number of cases.
Definitions:
Six Sigma: Six Sigma seeks to improve the quality of process outputs by minimizing variability in manufacturing and business processes, and identifying and removing the causes of defects (errors). (reduce variability to increase production quality)
Fisher's fundamental theorem: The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time. (More variance = better at adapting)
When use them??
Use Six Sigma to reduce variation when rugged landscape is fixed.
--> Concentrate when not much in environment changes.
Use Fisher's fundamental theorem when rugged landscape moves.
More variation allows for more chance of adpation.
--> Variance when environment has changed a lot.
---
http://en.wikipedia.org/wiki/Adaptive_landscapes
http://en.wikipedia.org/wiki/Hill_climbing
Wednesday, November 7, 2012
Untangling Skill and Luck
Untangling Skill and Luck
http://www4.gsb.columbia.edu/ideasatwork/feature/7317814/Untangling+Skill+and+Luck#?ref=tw
Why is it so important to distinguish skill from luck?We all know that the outcomes in many activities in life combine both skill and luck. Understanding the relative contributions of each can help us assess past outcomes and, much more importantly, anticipate future outcomes.
It is axiomatic that outcomes will revert to the mean in a system that combines skill and luck. An extremely favorable or unfavorable single outcome is going to be followed by an outcome that has an expected value closer to the average of all results. If a system reverts quickly to the mean, you know that it has lots of luck. If a system is slow to revert to the mean, you know that a good amount of skill is contributing to the outcomes.
Sports provide us with some good examples. In baseball, over a long season the best teams win about 60 percent of their games. But over shorter stretches it’s not unusual to see a team win or lose a whole bunch in a row. Reversion to the mean tells you the expected value of the whole season is still closer to 50-50 or slightly above or below. The Red Sox started off 2011 by losing six games in a row, so their fans were fretting. The Baltimore Orioles started off winning four in a row and their fans are excited. It’s a long season. Reversion to the mean will ultimately assert itself because there’s a lot of randomness in baseball.
In contrast, running races are almost all skill. That means the fastest runners will dominate: line up the same people five or 10 times in a row and the fastest person will win every time. There is no reversion to the mean because there is no luck. Runners finish in the same order every time.
How do you sort out skill from luck?
In sports, you can use statistical analysis to calculate where a given activity falls on the skill-luck continuum. Basically, you figure out what the results would look like if only luck mattered — the win-loss records for teams in a league would follow a binomial distribution — and then you figure out what the results would look like in a world of pure skill. You can then blend the two distributions in a proportion that matches the empirical results. The weightings of the blend indicate the relative contributions of skill and luck.
This leads directly into defining what makes for a good statistic. You see statistics used in many realms, but not all stats are equally useful. Basically, a good statistic should have two features. First, it should be persistent — that is, the current period result should have a decent correlation with the prior result. Second, it should be predictive — that is, if you do well or poorly as measured by the stat, the outcome will be good or bad. You’d be amazed how many stats fail this simple two-feature test.
While measuring skill and luck in business or investing isn’t as simple as sports, you can use these methods to take concrete steps in understanding the relative contributions of both skill and luck in these realms.
Given that business and investing are more complex systems, can you talk more about how skill and luck play out there?
First, it’s important to understand the paradox of skill, which says that as people in an endeavor become more skillful, luck actually becomes more important in determining outcomes. This seems backwards, but another sports example can illustrate the point. Seventy years ago, Ted Williams was the last baseball player to have a full-year batting average over .400. He hit .406 in 1941 and no one’s really come close ever since. Why?
One possible explanation is that players aren’t as good as they used to be, but that’s implausible because training techniques and technology have improved dramatically.
No one has come close to the record because everyone has gotten better: pitchers, batters, fielders. Consequently, while the mean of the batting averages has been relatively stable over the decades, the standard deviation of batting averages is much smaller: neither the best nor worst hitters are as far away from the average hitters as they used to be. When you have more skill, results become more uniform and so luck has more room to influence outcomes. Since the right tail of batting averages is closer to the mean today than it was in 1941, no player has been good enough to sustain a batting average over .400. And Williams, were he playing today, wouldn’t be able to do it either.
The paradox of skill is one reason it is so hard to beat the market. Everybody is smart, has incredible technology, and the government has worked to ensure that the dissemination of information is uniform. So information gets priced into stocks quickly and it’s very difficult to find mispricing. By the way, the standard deviation of mutual fund returns has been declining for the last 50 years or so, just as it has for batting averages.
So where would you place business and investing on the continuum?
Some academics have argued that all market outperformance is attributable to luck. But it’s been demonstrated, by statistical tests and common sense, that there is a component of skill involved. There’s a big difference between saying investing is all luck and saying it has a lot of luck. But as an investor it is important to acknowledge that, on the continuum, investing is closer to the luck side. That doesn’t mean that investors aren’t skillful — it is rather a reflection of the paradox of skill.
This leads to an important mindset: whenever you observe any outcome, you should always ask, “What would I expect by chance?” If the actual outcomes are different than what chance would dictate, there’s likely some element of skill.
So what constitutes skill in a field where probability dominates? The key is to have a good process. In all probabilistic fields, the best performers dwell on process. This is true for great value investors, great poker players, or great sports team managers. It’s all process stuff. It’s hard to do psychologically, emotionally, organizationally, but that is how you get paid.
You highlight the market’s march toward efficiency, which suggests a much more competitive landscape. What does it mean for the future of investment management?
Until we change human nature, or evolution weeds out these kinds of behaviors — which isn’t going to happen anytime soon — there will be opportunities in markets.
Think about handicapping in horseracing. There are two separate issues to consider. One is how fast the horse is likely to run in a particular race. The second is the odds priced on the tote board. If the horse’s chances of success are fully reflected in the odds, you are not going to make any money. You are looking for discrepancies between the horse’s prospects and what’s priced on the board.
The failure to distinguish between the fundamentals of how a company will perform and expectations — that is, what is priced into the security — is probably the biggest error in the investment business. Our natural tendency is to buy when things are going well and sell when things are going badly, irrespective of what’s priced in.
So the behavioral piece will continue to be enormous. Our behavioral finance courses routinely address the common heuristics that humans use and the biases that emanate from these heuristics. These biases include overconfidence, anchoring, confirmation bias, and recency bias. If you’re a normal human being, you exhibit all of these biases. The goal is to develop a process that weaves in tools and techniques to allow you to mitigate or manage those biases.
Seth Klarman, founder of the Baupost Group, has a great line: “Value investing is at its core the marriage of a contrarian streak and a calculator.” The contrarian streak says that you must be willing and able to do something different than what the consensus is telling you to do. That’s extremely difficult. And sometimes the consensus is right: if the movie house is on fire, you should run out the door, not in. So adding the calculator part is key; being a contrarian makes sense if it leads to a mispricing between fundamentals and expectations. That is a market opportunity.
The types of opportunities that present themselves tend to evolve. Think about them like the game Whac-A-Mole, where the moles are opportunities in markets. A mole pops up, you whack it, another one pops up. If an academic publishes a paper on a certain strategy that generates alpha, investors will exploit the strategy and by their very actions will compete away the excess returns. But the good news is that another opportunity pops up somewhere else.
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