Computer Simulations Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

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Here, you will find Computer Simulations Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Computer Simulations from Coursera Free Certification Course.

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About Computer Simulations Course

This course shows how computer simulations are used to explore the realm of what is theoretically possible.

Course Apply Link – Computer Simulations

Computer Simulations Quiz Answers

Week 01: Computer Simulations Coursera Quiz Answers

Module 1 Quiz

Q.1 What is theoretical modeling NOT about?

  • Creating a truthful copy of the modeled phenomena
  • Searching for a simplified depiction of reality
  • Selecting certain aspects of reality
  • Reducing the number of unnecessary details about reality

Q.2 Two colleagues show you their models of the same social phenomena. Immediately you know that:

  • Both of them can be useful, representing different aspects
  • One of them must be right and the other one wrong
  • Both of them can be perfectly accurate description of the phenomena
  • One of them must be more right than the other

Q.3 Galileo Galilei famously argued that the book of nature “is written in the language of mathematics”. It is important to choose the right language to describe something. In computational science, what is our main language?

  • Mainly English, but there is also much ongoing research in Chinese
  • Music, including written notes and hands-on sounds
  • Analytical mathematics
  • Computer code

Q.4 When exploring Schelling’s segregation model, we made several discoveries. Which one was NOT one of them?

  • A small change in individual preferences can have large societal effects
  • The outcome is always a bit different, also depending on the initial conditions
  • Differences in random initial settings, led to different final outcomes
  • Less tolerant individuals, lead to a more segregated society

Q.4 Most of them time, Schelling’s segregation model found an ‘equilibrium’. What does this mean?

  • Opposing forces or influences are balanced and nothing changes anymore
  • Agents in the model find a recurring routine in predictable equanimity
  • The computer software runs in a stable manner and does not crash

Q.5 Working with the extended version of Schelling’s segregation model, we concluded that in modern societies, which have both digital access to wider social circles and more means of transport mobility, segregation will:

  • Happen faster and be more extreme, since people can decrease inconveniences quicker
  • Be avoided, since people can find the equilibrium between tolerance and segregation
  • Never settle into a fixed state, since wider social circles lead to more tolerance and more mobility to quicker segregation
  • Occur exactly in the same manner as in the simpler model, showing that there is no need in extending models with more bells and whistles

Week 02: Computer Simulations Coursera Quiz Answers

Module 2 Quiz

Q.1 You got to know NetLogo through our explorations, where ‘patches’ and ‘ticks’ are fundamental components. What are they?

  • A patch is a spatial unit, and a tick is a temporal units
  • A tick represents quirk in agent behavior and a patch rectifies it
  • A patch defines the global environment, and a tick individual behavior
  • Patches represent global locations and ticks local locations
  • Ticks checkmark the completeness of all model assumptions, and patches strengthen the resulting weak points

Q.2 When it comes to agent-based computer simulations, such as Sugarscape, which of the following statements is true?

  • Traits of the agents are fixed while the model runs, while traits of environment can change variably
  • Traits of the environment are fixed while the model runs, while traits of agents can change variably
  • The traits of both the environment and the agents can either be fixed while the model runs, or change variably according to certain rules

Q.3 The Sugarscape is a famous and emblematic agent-based model of a simple artificial society. Having spent much time with it, you are aware of the assumptions of the very basic first version of the Sugarscape model. Which one was NOT one of them?

  • Agents chose to move to the unoccupied patch with most sugar
  • Each agent has a specific level of resource detection capacity
  • Each agent has a specific level of resource usage capacity
  • Resources are distributed according to an unequal Pareto law
  • Different locations on Sugarscape have different amounts of sugar

Q.4 In the social science of economics, what is the most typical measure to assess income inequality?

  • Gini coefficient, based on the Lorenz curve
  • Disadvantage measure, based on the individual gains
  • Sustainability metric, based on previous gains
  • 1% metric, based on the number of self-made billionaires
  • Fitness, based on individual capabilities

Q.5 We grew an extremely unequal wealth distribution on Sugarscape, one that resembled the unequal income distribution in society. Where did this effect come from?

  • From the behavioral greediness of sugar maximizing agents
  • From the Sugarscape landscape
  • From the interacting combination of different factors
  • From the unequal distribution of agent’s abilities (vision and metabolism)

Q.6 What does it mean that income is usually distributed according to a power-law, called a Pareto distribution?

  • Preferential attachments is driven by Pareto distributions of income levels, since it makes the rich even richer
  • Exponentially few, have exponentially much, and exponentially many, have exponentially little
  • Exponentially few nodes, have many links, and exponentially many, have few links

Q.7 As we have confirmed with our computer simulations, the extreme inequality in income distributions that have been found in societies over centuries is an inevitable law of nature. Whatever we do, inequality will always emerge as a result. True or False.

  • False
  • True

Q,8 In science, the so-called “spherical cow” argument is a metaphor for:

  • The fragility (bubble) of big and sturdy phenomena (cow)
  • Milking existing theories that turn out to be inflated
  • Overly simplified and impractical models of complex real-life phenomena
  • The birth of a new theory that was regurgitated and masticated many times

Q.9 In one of our Sugarscape model, we introduced an inheritance mechanism. Agents passed on their sugar to their offspring after death. What was the effect we observed?

  • Too much sugar damaged the metabolism of the offspring
  • There was less evolutionary selection pressure as more unskilled agents made it
  • Sexual reproduction slowed down since agents prefer self-made mates
  • Children that inherited much sugar did not survive long since they were not prepared

Q.10 In agent-based models, like the NetLogo implementations we’ve seen, it is not possible to program the following for an individual agent:

  • All of these options
  • Unchanging characteristics, such as gender and metabolism
  • Unchanging customs, such as when they wake up and go to sleep
  • Changing behaviors, such as the point when an agent becomes aggressive
  • Changing characteristics, such as political opinions and sexual attraction

Q.11 In the social sciences, the terms macro and micro, respectively, describe:

  • The big picture of aggregate variables and the detailed picture of individuals
  • Something that heats and something that cools social interactions
  • An automated task in society (think MS Excel macro) and a manually executed task

Q.12 What is Epstein’s ‘generativist motto’?

  • If you didn’t code it, you didn’t grow it
  • If you didn’t grow it, you didn’t explain it
  • If you didn’t explain it, you didn’t generate it
  • If you didn’t generate it, you didn’t code it

Week 03: Computer Simulations Coursera Quiz Answers

Module 3 Quiz Answers

Q.1 Playing around with a computer simulation until you get a good feel for what numbers work, is known as obtaining:

  • Entertainment
  • An analytical solution
  • A mathematical derivation
  • A numerical solution

Q.2 During his lecture, Prof. Frey pursued a systematic approach toward risk taking. He simplified the problem and wrote a few lines of code to model the idea. His initial hypothesis was that it is better to take safe bets for important decisions. What was the result suggested by his computer model?

  • Digital footprints replicate real data, but they are not representative
  • His initial hypothesis was confirmed, which shows the power of intuition
  • With repetitions, it is more beneficial to take risky bets for important decisions
  • Risky bets turn into safe bets, if one allows the simulation to run long enough

Q.3 Social scientists from Adam Smith, over Jean-J. Rousseau, to Karl Marx, were all fascinated with the idea of social emergence. Why is it that the whole is different from the sum of its parts?

  • Leadership of special individuals
  • Entrepreneurial visions with above-average energy
  • Interactions of interdependent parts
  • Both genetic and cultural characteristics

Q.4 We reviewed some of the most common sources of non-linear tipping points in social emergence, such as in Granovetter’s riot model. What was the main contributing driver we identified?

  • Inter-dependency among actors who are somewhat diverse
  • Social networks that are both stable and efficient
  • Homogeneous traits and interdependence among actors
  • Social network dynamics that evolve according to benefit-cost analysis
  • Many individuals who are very diverse and tightly connected

Q.5 We said that traditional computer simulations worked with ‘factors’ instead of ‘actors’ (like the agent-based models we worked with). Which of the following model uses actors, and not factors?

  • The simulation of one thousand municipalities, each with different traits and laws
  • The simulation of raw material supply and the demand for consumer goods
  • The simulation of the relationship between the weather, workers health, and the price of infrastructure projects
  • The correlation between educational level, income, and salaries

Q.6. Why is it important to use agent-based models (ABMs) to model social emergence, and not traditional factor-based computer simulation tools?

  • Agent-based models are calibrated with the digital footprint, while traditional models with mathematical assumptions
  • ABMs allow to model individual actors, each with different traits and behaviors
  • Traditional simulation methods do not have sufficient storage and processing power
  • ABMs have better visualization tools, which are important to understand the effects of social emergence

Q.7. Imagine you have coded up a brand new agent-based model for traffic in San Francisco, and someone asks you to adopt it to Santiago de Chile, you will need to budget for several items. What will you NOT need?

  • The same amount of human programming time
  • Data to calibrate your model to local conditions
  • Additional computer storage to store your new model
  • Maps from Santiago, especially roads
  • Empirical evidence about driving behavior in Santiago

Q.8. What does it mean when a computational social scientist talks about the ‘invariant distribution’ produced by a computer simulation?

  • Even the variable traits of agents do not change in time while running the model
  • The time until the model settles into equilibrium does not vary with the amount of random choices coding into the model
  • The input that does not depend on empirical calibration of the model
  • The model is probabilistic, but produces a predictable distribution of possible outcomes

Q.9. We found that so-called ‘frozen accidents’ play a crucial role in the evolution of social systems. Which of the following is NOT an example of it?

  • The genetic code of a common polar bear was unexpectedly found in a block of arctic ice, leading to big surprise with the producer of preserved food company that processed the ice
  • Joey’s first live soccer game ends with a tight penalty shootout. He becomes a fan of the winning team, and will later lead them to the Champions League Final
  • A young child gets her first computer. Her parents want to buy her a Mac, but a Dell PC is on sale, and they get her the PC. She’ll never use Apple products in her life.

Q.10 The secret sauce of the new company you work for is a simulation model that allows to create an impressively accurate invariant distribution for the political voting-preferences of citizens in a certain city. If that’s all the company has, what is it that it can predict with this model outcome?

  • If the wife of Mr. Liberal will vote liberal
  • Which of the individuals in a certain district will vote for a certain party
  • How you will vote, if you live in this city
  • What percentage will vote for a certain party

Q.11. In the lecture, we reviewed several advantages of working with agent-based computer simulations. Which one was NOT one of them?

  • The ability to model individual diversity and interdependency among agents
  • The ability to model flexibly with probabilities, which is more realistic
  • Instinctual communication of results, often with visual aids
  • Straightforward detection of causes and effects
  • Easy adaptability of modular parts to local context

Week 04: Computer Simulations Coursera Quiz Answers

Module 4 Quiz Quiz Answers

Q.1 We have discussed four possible outcomes of models. Which one was NOT one of them?

  • Equipartitioned disorder
  • Cyclical periodic orbit
  • Homogeneous equilibrium
  • Randomness

Q.2 Why are all models wrong? …even so some are useful…

  • Scientists do not embrace interdisciplinary approaches enough
  • Models always abstract and never can include every detail
  • Scientific models are always underfunded
  • Scientists do not embrace multidisciplinary approaches enough

Q.3. We studied possible outcomes of a model that represented the famous Lotka-Volterra predator-prey dynamics. We used only three interdependent variables, including slowly growing grass, sheep that fed off grass, and wolves that fed off sheep. What kind of stylized model outcome did we observe when we set it in motion?

  • Complicated
  • Homogeneous state/Equilibrium
  • Random/Chaotic
  • A cycling periodic Orbit

Q.4 Your new employer works in the field of drone logistics. They are looking for the shortest possible route that visits each customer and returns to the origin. A college asks you to work on the numerical solution, while she will take care of the analytical solution. What do you start thinking about?

  • How to get out of this job, because you never heard about these terms in any online lecture
  • How to make sense of the Dantzig-Fulkerson-Johnson formulation of the famous traveling salesman problem
  • How to come up with a mathematical equation to model the problem
  • How to set up a computer simulation that allows to find an approximate answer

Q.5 In science, what does the “Butterfly Effect” refer to?

  • The intended pun that underlines the difference between a churned dairy product in flight, and flying moth-like insects
  • The geological proof that a small insect can create a transatlantic storm
  • A humorous metaphor referring to the elegant beauty of a flying spherical cow
  • A metaphor for strong sensitivity to initial conditions in chaotic systems

Q.6 A computational social science colleague in your future job shows you two models that predict fake news. Both have the same size (their complexity is 184 lines of compressed code), and both achieve the same level of accuracy (an impressive 97.3%). One works to predict fake news on Facebook (Model A). The other one allows to predict fake news on all social media sites (Model B). Which one is better according to Occam’s razor??

  • Both are equally good
  • Occam’s razor does not apply here

Q.7. You have two models, model A predicts consumer behavior with 75% accuracy (it consists of 5 lines of code), and model B predicts it with 90% accuracy (it consists of 6 lines of code). According to Occam’s razor, which model should you prefer?

  • It does not apply
  • Both are equally good
  • One of the promises held by “computational social science is” that:
  • It requires less creativity than the imaginary potential involved in music, art, and poetry
  • We are now able to create a truly accurate model of reality

More About This Course

Big data and artificial intelligence get most of the press about computational social science, but maybe the most complex aspect of it refers to using computational tools to explore and develop social science theory.

This course shows how computer simulations are used to explore the realm of what is theoretically possible.

Computer simulations allow us to study why societies are the way they are, and to dream about the world we would like to live in. This can be as intuitive as playing a video game.

Much like the well-known video game SimCity is used to build and manage an artificial city, we use agent-based models to grow and study artificial societies. Without hurting anyone in the real world, computer simulations allow us to explore how to make the world a better place.

We play hands-on with several practical computer simulation models and explore how we can combine hypothetical models with real-world data. Finally, you will program a simple artificial society yourself, bottom-up.

This will allow you to feel the complexity that arises when designing social systems, while at the same time experiencing the ease with which our new computational tools allow us to pursue such daunting endeavors.

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Computer Simulations Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

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