This document discusses the challenges and promises of agent-based modeling in economics. It begins by outlining the key challenges, such as developing appropriate abstractions while keeping models realistic, and a lack of prior examples. However, it argues that agent-based modeling can complement existing tools by capturing nonlinearities and heterogeneous agents. It then describes a housing market model project that aims to build an agent-based model that can forecast time series and be used for policy analysis. The project involves calibrating decision rules using microdata to simulate the housing market at the individual household level.
A presentation at the Open University, Milton Keynes, 2003. The paper presents three different examples of simulation: An agent-based model of adaptive behaviour in oligopoly, a learning model of consumption and lifestyle and a preliminary attempt to model social mobility processes.
ITS 832Chapter 5From Building a Model to Adaptive Robust.docxvrickens
ITS 832
Chapter 5
From Building a Model to Adaptive Robust
Decision MakingUsing Systems Modeling
Information Technology in a Global Economy
Introduction
• Systems modeling
• Focus on decision makingabilities
• Legacy System Dynamics (SD)modeling
• Recent innovations
• What the futureholds
• Examples
Systems modeling
• Dynamic complexity
• Behavior evolves overtime
• Modeling methods
• System Dynamics (CD)
• Discrete Event Simulation(DES)
• Multi-actorSystems Modeling (MAS)
• Agent-based Modeling (ABM)
• ComplexAdaptive Systems Modeling (CAS)
• Enhanced computing supports model based decision making
• Modeling and simulation has become interdisciplinary
• Operation research, policy analysis, data analytics, machine learning,
computer science
Legacy System DynamicsModeling
• 1950s – Jay W.Forrester
• Primary characteristics
• Feedback effects – dependent on their own past
• Accumulation effects – building up intangibles
• Behavior ofa system is explained
• Casual theory – model generates dynamic behavior
• Works well when
• Complex system responds to feedback and accumulation
Recent Innovations
• Detailed list of individual innovations
• Deep uncertainty
• Analysts do not know or cannot agree on
• Model
• Probability distributions of key features
• Value of alternative outcomes
• Two primary evolutions
• Smarter methods (DataScience)
• Usability/accessibility advances
What theFuture Holds
• Better models
• More data (“BigData”)
• Social media
• Advanced capabilities for
• Hybrid modeling
• Simultaneous modeling
Modeling andSimulation
Examples
Assessing the Risk, and Monitoring, of New Infectious
Diseases
Simple systems model with deep uncertainty
Integrated Risk-CapabilityAnalysis Under Deep
Uncertainty
System-of-systems approach
Policing Under DeepUncertainty
Smart model-based decision support system
Summary
• Modeling has long been used with complex systems
• Recent evolutions have advancedmodeling
• Increase computing power
• Social media and Big data
• Sophisticated analytics
• Multi-method and hybrid approaches are now feasible
• Continued move intointerdisciplinary study
• Advanced modeling for complex systems
ITS 832
CHAPTER 6
Features andAdded Value of Simulation
Models UsingDifferent Modelling
Approaches Supporting Policy-Making
Information Technology in a Global Economy
INTRODUCTION
• Simulation Models in policy-making – foundations
• eGovPoliNet
• International multidisciplinary policy community in ICT
• Selected Modeling approaches
• VirSim – Pandemic policy
• microSim – Swedish population
• MEL-C – Early Life-course
• Ocopomo’s Kosice Case – Energy policy
• SKIN – Dynamic systems component interaction
FOUNDATIONS OF SIMULATION
MODELING
• Simulation model
• Smaller, less detailed, less complex (or all)
• Computer software
• Approximates real-world behavior
• Benefits
• Easier, simpler than monitoring reality
• Possibly the only f ...
It includes important Definitions of economics and managerial economics. Also includes related topics like Micro and Macro Economics, objectives of a firm and various profit maximization models.
ITS 832CHAPTER6Features andAddedValue of Simulation .docxcareyshaunda
ITS 832
CHAPTER6
Features andAddedValue of Simulation
Models UsingDifferent Modelling
Approaches SupportingPolicy-Making
InformationTechnology in aGlobal Economy
INTRODUCTION
• Simulation Models in policy-making – foundations
• eGovPoliNet
• Internationalmultidisciplinarypolicycommunity in ICT
• Selected Modelingapproaches
• VirSim – Pandemic policy
• microSim – Swedish population
• MEL-C – Early Life-course
• Ocopomo’sKosiceCase – Energy policy
• SKIN – Dynamic systems component interaction
FOUNDATIONS OFSIMULATION
MODELING
• Simulation model
• Smaller, less detailed, less complex (or all)
• Computer software
• Approximates real-worldbehavior
• Benefits
• Easier, simpler than monitoring reality
• Possibly the only feasible way to “playout”a scenario
• Approaches discussed
• System dynamics
• Agent-based modeling(ABM)
• Micro-simulation
STEPS INDEVELOPINGSIMULATION
MODELS
SIMULATION MODELSEXAMINED
VIRSIM
• A Model to Support PandemicPolicy-Making
• Simulates thespread of pandemic influenza
• Goal
• Determine theoptimal time andduration of school closings to affect
influenzaspread
• System dynamicsmodel
• Separates population into3segments
• Younger than20 yearsold
• 20 – 59 years old
• 60 years old and older
• No environmental features considered
• Only inputdata forSweden
MICROSIM
• Micro-simulation Model
• Modeling theSwedish Population
• Goal
• Determine how multiplebehavior features affect influenza
spread
• Micro-simulation model
• More granular thanVirSim
• Focused only on Sweden
• Robustfor intended population
MEL-C
• Modeling the EarlyLife-Course
• Knowledge-based inquiry toolWith Intervention
modeling (KIWI)
• Goal
• Identify social development milestones in early life
that most affect lateroutcomes
• Health,nutrition, education, living conditions, etc.
• Micro-simulationmodel
• Genericapplicability
• Limitedby rangeofoptions
• Evidence-based
• Not very flexible when considering untested
approaches
OCOPOMO’S KOSICECASE
• Kosice self-governing region energy policy simulation
• Goal
• Develop better energypolicy
• And measurepolicy effectiveness
• House insulation and renewable energy sources
• ABM model
• Modelis geographically anchored
• Difficult to apply to other regions
• Many geographicfeatures
• Stakeholder engagement iskey
SKIN
• Simulating Knowledge Dynamics in Innovation Networks
• Goal
• Improve innovation throughinteractions
• ABM model
• Based on general market model
• Agents areboth
• Sellers (providers)
• Buyers (consumers)
• Agentsconsider dynamic interaction
• Modify behavior to improve innovation
• i.e.sellmore or buy better
SUMMARY
• Simulations allow multiple models to be investigated
• Without real-worldconsequences
• Examined five models built on three approaches
• VirSim – System dynamics
• MicroSim -Microsimulation
• MEL-C - Microsimulation
• Ocopomo’sKosiceCase -ABM
• SKIN – ABM
• Each approach has advantages and limitatio.
Evolutionary analogies are often accused of a lack of realism with respect to real social phenomena. However, in particular circumstances, the analogy may be particularly pertinent. This paper presents a simulation in which successful forms of industrial organisation are literally able to reproduce themselves through the franchising process.
Global Futures & Strategic Foresight (GFSF) program enhances and uses a coordinated suite of biophysical and socioeconomic models to assess potential returns to investments in new agricultural technologies and policies. These models include IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), hydrology and water supply-demand models, and the DSSAT suite of process-based crop models.
The program also provides tools and trainings to scientists and policy makers to undertake similar assessments.
GFSF program is a Consultative Group on International Agricultural Research (CGIAR) program led by the International Food Policy Research Institute (IFPRI)
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
A presentation at the Open University, Milton Keynes, 2003. The paper presents three different examples of simulation: An agent-based model of adaptive behaviour in oligopoly, a learning model of consumption and lifestyle and a preliminary attempt to model social mobility processes.
ITS 832Chapter 5From Building a Model to Adaptive Robust.docxvrickens
ITS 832
Chapter 5
From Building a Model to Adaptive Robust
Decision MakingUsing Systems Modeling
Information Technology in a Global Economy
Introduction
• Systems modeling
• Focus on decision makingabilities
• Legacy System Dynamics (SD)modeling
• Recent innovations
• What the futureholds
• Examples
Systems modeling
• Dynamic complexity
• Behavior evolves overtime
• Modeling methods
• System Dynamics (CD)
• Discrete Event Simulation(DES)
• Multi-actorSystems Modeling (MAS)
• Agent-based Modeling (ABM)
• ComplexAdaptive Systems Modeling (CAS)
• Enhanced computing supports model based decision making
• Modeling and simulation has become interdisciplinary
• Operation research, policy analysis, data analytics, machine learning,
computer science
Legacy System DynamicsModeling
• 1950s – Jay W.Forrester
• Primary characteristics
• Feedback effects – dependent on their own past
• Accumulation effects – building up intangibles
• Behavior ofa system is explained
• Casual theory – model generates dynamic behavior
• Works well when
• Complex system responds to feedback and accumulation
Recent Innovations
• Detailed list of individual innovations
• Deep uncertainty
• Analysts do not know or cannot agree on
• Model
• Probability distributions of key features
• Value of alternative outcomes
• Two primary evolutions
• Smarter methods (DataScience)
• Usability/accessibility advances
What theFuture Holds
• Better models
• More data (“BigData”)
• Social media
• Advanced capabilities for
• Hybrid modeling
• Simultaneous modeling
Modeling andSimulation
Examples
Assessing the Risk, and Monitoring, of New Infectious
Diseases
Simple systems model with deep uncertainty
Integrated Risk-CapabilityAnalysis Under Deep
Uncertainty
System-of-systems approach
Policing Under DeepUncertainty
Smart model-based decision support system
Summary
• Modeling has long been used with complex systems
• Recent evolutions have advancedmodeling
• Increase computing power
• Social media and Big data
• Sophisticated analytics
• Multi-method and hybrid approaches are now feasible
• Continued move intointerdisciplinary study
• Advanced modeling for complex systems
ITS 832
CHAPTER 6
Features andAdded Value of Simulation
Models UsingDifferent Modelling
Approaches Supporting Policy-Making
Information Technology in a Global Economy
INTRODUCTION
• Simulation Models in policy-making – foundations
• eGovPoliNet
• International multidisciplinary policy community in ICT
• Selected Modeling approaches
• VirSim – Pandemic policy
• microSim – Swedish population
• MEL-C – Early Life-course
• Ocopomo’s Kosice Case – Energy policy
• SKIN – Dynamic systems component interaction
FOUNDATIONS OF SIMULATION
MODELING
• Simulation model
• Smaller, less detailed, less complex (or all)
• Computer software
• Approximates real-world behavior
• Benefits
• Easier, simpler than monitoring reality
• Possibly the only f ...
It includes important Definitions of economics and managerial economics. Also includes related topics like Micro and Macro Economics, objectives of a firm and various profit maximization models.
ITS 832CHAPTER6Features andAddedValue of Simulation .docxcareyshaunda
ITS 832
CHAPTER6
Features andAddedValue of Simulation
Models UsingDifferent Modelling
Approaches SupportingPolicy-Making
InformationTechnology in aGlobal Economy
INTRODUCTION
• Simulation Models in policy-making – foundations
• eGovPoliNet
• Internationalmultidisciplinarypolicycommunity in ICT
• Selected Modelingapproaches
• VirSim – Pandemic policy
• microSim – Swedish population
• MEL-C – Early Life-course
• Ocopomo’sKosiceCase – Energy policy
• SKIN – Dynamic systems component interaction
FOUNDATIONS OFSIMULATION
MODELING
• Simulation model
• Smaller, less detailed, less complex (or all)
• Computer software
• Approximates real-worldbehavior
• Benefits
• Easier, simpler than monitoring reality
• Possibly the only feasible way to “playout”a scenario
• Approaches discussed
• System dynamics
• Agent-based modeling(ABM)
• Micro-simulation
STEPS INDEVELOPINGSIMULATION
MODELS
SIMULATION MODELSEXAMINED
VIRSIM
• A Model to Support PandemicPolicy-Making
• Simulates thespread of pandemic influenza
• Goal
• Determine theoptimal time andduration of school closings to affect
influenzaspread
• System dynamicsmodel
• Separates population into3segments
• Younger than20 yearsold
• 20 – 59 years old
• 60 years old and older
• No environmental features considered
• Only inputdata forSweden
MICROSIM
• Micro-simulation Model
• Modeling theSwedish Population
• Goal
• Determine how multiplebehavior features affect influenza
spread
• Micro-simulation model
• More granular thanVirSim
• Focused only on Sweden
• Robustfor intended population
MEL-C
• Modeling the EarlyLife-Course
• Knowledge-based inquiry toolWith Intervention
modeling (KIWI)
• Goal
• Identify social development milestones in early life
that most affect lateroutcomes
• Health,nutrition, education, living conditions, etc.
• Micro-simulationmodel
• Genericapplicability
• Limitedby rangeofoptions
• Evidence-based
• Not very flexible when considering untested
approaches
OCOPOMO’S KOSICECASE
• Kosice self-governing region energy policy simulation
• Goal
• Develop better energypolicy
• And measurepolicy effectiveness
• House insulation and renewable energy sources
• ABM model
• Modelis geographically anchored
• Difficult to apply to other regions
• Many geographicfeatures
• Stakeholder engagement iskey
SKIN
• Simulating Knowledge Dynamics in Innovation Networks
• Goal
• Improve innovation throughinteractions
• ABM model
• Based on general market model
• Agents areboth
• Sellers (providers)
• Buyers (consumers)
• Agentsconsider dynamic interaction
• Modify behavior to improve innovation
• i.e.sellmore or buy better
SUMMARY
• Simulations allow multiple models to be investigated
• Without real-worldconsequences
• Examined five models built on three approaches
• VirSim – System dynamics
• MicroSim -Microsimulation
• MEL-C - Microsimulation
• Ocopomo’sKosiceCase -ABM
• SKIN – ABM
• Each approach has advantages and limitatio.
Evolutionary analogies are often accused of a lack of realism with respect to real social phenomena. However, in particular circumstances, the analogy may be particularly pertinent. This paper presents a simulation in which successful forms of industrial organisation are literally able to reproduce themselves through the franchising process.
Global Futures & Strategic Foresight (GFSF) program enhances and uses a coordinated suite of biophysical and socioeconomic models to assess potential returns to investments in new agricultural technologies and policies. These models include IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), hydrology and water supply-demand models, and the DSSAT suite of process-based crop models.
The program also provides tools and trainings to scientists and policy makers to undertake similar assessments.
GFSF program is a Consultative Group on International Agricultural Research (CGIAR) program led by the International Food Policy Research Institute (IFPRI)
The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making.
In this workshop, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry.
Agenda:
In Part 1, we will discuss key trends in AI and machine learning in the financial services industry, including the key use cases, challenges, and best practices.
In Part 2, we will illustrate two case studies where AI and machine learning techniques are applied in financial services.
Case studies:
Sentiment Analysis Using Natural Language Processing in Finance
In this case study, we will demonstrate the use of natural language processing techniques to analyze EDGAR call earnings transcripts that could be used to generate sentiment analysis scores using the Amazon Comprehend, IBM Watson, Google, and Azure APIs (application programming interfaces). We will illustrate how these scores can be used to augment traditional quantitative research and for trading decisions.
Credit Risk Decision Making Using Lending Club Data
In this case study, we will use the Lending Club data set to build a credit risk model using
machine learning techniques.
This paper presents two models of key determinants in the evolution of the shadow banking system. First of all, a shadow banking measure is built from a European perspective. Secondly, information on several variables is retrieved basing their selection in previous literature. Thirdly, those variables are grouped in: 1) the base model: real GDP, Institutional investors’ assets, term-spread, banks’ net interest margin and liquidity; and 2) the extended model: the former five plus an indicator of systemic stress, an index of banking concentration and inflation. Finally, regression analysis on those models is conducted for different countries’ samples. Both OLS and panel data analysis is undergone. Results suggest important and consistent geographical differences in relations between shadow banking and key determinant variables’ effects. Thus, this essay provides financial authorities with a valuable benchmark to which they should pay attention before designing optimal policies seeking to reduce the financial risk that shadow banking entails.
A presentation of the different aspects of model risk management to the Sydney Financial Mathematics Workshop (SFMW) in November 2017. This is brought to life by looking at two notable modelling failures in banking and rounded out by reflecting on the implications of modelling trends for the future of model risk management.
how can I sell my pi coins for cash in a pi APPDOT TECH
You can't sell your pi coins in the pi network app. because it is not listed yet on any exchange.
The only way you can sell is by trading your pi coins with an investor (a person looking forward to hold massive amounts of pi coins before mainnet launch) .
You don't need to meet the investor directly all the trades are done with a pi vendor/merchant (a person that buys the pi coins from miners and resell it to investors)
I Will leave The telegram contact of my personal pi vendor, if you are finding a legitimate one.
@Pi_vendor_247
#pi network
#pi coins
#money
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
Introduction to Indian Financial System ()Avanish Goel
The financial system of a country is an important tool for economic development of the country, as it helps in creation of wealth by linking savings with investments.
It facilitates the flow of funds form the households (savers) to business firms (investors) to aid in wealth creation and development of both the parties
how can I sell pi coins after successfully completing KYCDOT TECH
Pi coins is not launched yet in any exchange 💱 this means it's not swappable, the current pi displaying on coin market cap is the iou version of pi. And you can learn all about that on my previous post.
RIGHT NOW THE ONLY WAY you can sell pi coins is through verified pi merchants. A pi merchant is someone who buys pi coins and resell them to exchanges and crypto whales. Looking forward to hold massive quantities of pi coins before the mainnet launch.
This is because pi network is not doing any pre-sale or ico offerings, the only way to get my coins is from buying from miners. So a merchant facilitates the transactions between the miners and these exchanges holding pi.
I and my friends has sold more than 6000 pi coins successfully with this method. I will be happy to share the contact of my personal pi merchant. The one i trade with, if you have your own merchant you can trade with them. For those who are new.
Message: @Pi_vendor_247 on telegram.
I wouldn't advise you selling all percentage of the pi coins. Leave at least a before so its a win win during open mainnet. Have a nice day pioneers ♥️
#kyc #mainnet #picoins #pi #sellpi #piwallet
#pinetwork
what is the future of Pi Network currency.DOT TECH
The future of the Pi cryptocurrency is uncertain, and its success will depend on several factors. Pi is a relatively new cryptocurrency that aims to be user-friendly and accessible to a wide audience. Here are a few key considerations for its future:
Message: @Pi_vendor_247 on telegram if u want to sell PI COINS.
1. Mainnet Launch: As of my last knowledge update in January 2022, Pi was still in the testnet phase. Its success will depend on a successful transition to a mainnet, where actual transactions can take place.
2. User Adoption: Pi's success will be closely tied to user adoption. The more users who join the network and actively participate, the stronger the ecosystem can become.
3. Utility and Use Cases: For a cryptocurrency to thrive, it must offer utility and practical use cases. The Pi team has talked about various applications, including peer-to-peer transactions, smart contracts, and more. The development and implementation of these features will be essential.
4. Regulatory Environment: The regulatory environment for cryptocurrencies is evolving globally. How Pi navigates and complies with regulations in various jurisdictions will significantly impact its future.
5. Technology Development: The Pi network must continue to develop and improve its technology, security, and scalability to compete with established cryptocurrencies.
6. Community Engagement: The Pi community plays a critical role in its future. Engaged users can help build trust and grow the network.
7. Monetization and Sustainability: The Pi team's monetization strategy, such as fees, partnerships, or other revenue sources, will affect its long-term sustainability.
It's essential to approach Pi or any new cryptocurrency with caution and conduct due diligence. Cryptocurrency investments involve risks, and potential rewards can be uncertain. The success and future of Pi will depend on the collective efforts of its team, community, and the broader cryptocurrency market dynamics. It's advisable to stay updated on Pi's development and follow any updates from the official Pi Network website or announcements from the team.
NO1 Uk Divorce problem uk all amil baba in karachi,lahore,pakistan talaq ka m...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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NO1 Uk Rohani Baba In Karachi Bangali Baba Karachi Online Amil Baba WorldWide...Amil baba
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Even tho Pi network is not listed on any exchange yet.
Buying/Selling or investing in pi network coins is highly possible through the help of vendors. You can buy from vendors[ buy directly from the pi network miners and resell it]. I will leave the telegram contact of my personal vendor.
@Pi_vendor_247
Latino Buying Power - May 2024 Presentation for Latino CaucusDanay Escanaverino
Unlock the potential of Latino Buying Power with this in-depth SlideShare presentation. Explore how the Latino consumer market is transforming the American economy, driven by their significant buying power, entrepreneurial contributions, and growing influence across various sectors.
**Key Sections Covered:**
1. **Economic Impact:** Understand the profound economic impact of Latino consumers on the U.S. economy. Discover how their increasing purchasing power is fueling growth in key industries and contributing to national economic prosperity.
2. **Buying Power:** Dive into detailed analyses of Latino buying power, including its growth trends, key drivers, and projections for the future. Learn how this influential group’s spending habits are shaping market dynamics and creating opportunities for businesses.
3. **Entrepreneurial Contributions:** Explore the entrepreneurial spirit within the Latino community. Examine how Latino-owned businesses are thriving and contributing to job creation, innovation, and economic diversification.
4. **Workforce Statistics:** Gain insights into the role of Latino workers in the American labor market. Review statistics on employment rates, occupational distribution, and the economic contributions of Latino professionals across various industries.
5. **Media Consumption:** Understand the media consumption habits of Latino audiences. Discover their preferences for digital platforms, television, radio, and social media. Learn how these consumption patterns are influencing advertising strategies and media content.
6. **Education:** Examine the educational achievements and challenges within the Latino community. Review statistics on enrollment, graduation rates, and fields of study. Understand the implications of education on economic mobility and workforce readiness.
7. **Home Ownership:** Explore trends in Latino home ownership. Understand the factors driving home buying decisions, the challenges faced by Latino homeowners, and the impact of home ownership on community stability and economic growth.
This SlideShare provides valuable insights for marketers, business owners, policymakers, and anyone interested in the economic influence of the Latino community. By understanding the various facets of Latino buying power, you can effectively engage with this dynamic and growing market segment.
Equip yourself with the knowledge to leverage Latino buying power, tap into their entrepreneurial spirit, and connect with their unique cultural and consumer preferences. Drive your business success by embracing the economic potential of Latino consumers.
**Keywords:** Latino buying power, economic impact, entrepreneurial contributions, workforce statistics, media consumption, education, home ownership, Latino market, Hispanic buying power, Latino purchasing power.
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@Pi_vendor_247
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1. The Challenge of Agent-based
Modeling in Economics
!
OECD!
Paris, May 19, 2014
J.
Doyne
Farmer
Ins$tute
for
New
Economic
Thinking
at
the
Oxford
Mar$n
School
Mathema0cal
Ins0tute,
Oxford
University
External
professor,
Santa
Fe
Ins$tute
Feb.
24,
2014
2. My
Instruc$ons
(what
I
am
supposed
to
cover)
• Challenges
of
applying
agent-‐based
modeling
• Promising
new
areas
of
applica$on
• How
will
ABM
evolve
in
the
future?
2
3. What
is
an
agent-‐based
model?
• A
computer
simulation
of
a
system
of
interacting
heterogeneous
agents
– e.g.
households,
firms,
banks,
mutual
funds,
government,
central
bank
• Agent
decision
rules
can
be:
– behavioral
or
rational
– hard-‐wired
or
evolving
under
learning
as
agents
attempt
to
maximize
utility
2
4. Paul
Krugman’s
view
of
agent-‐based
modeling
!
“Oh, and about RogerDoyne Farmer (sorry,
Roger!) and Santa Fe and complexity and all
that: I was one of the people who got all
excited about the possibility of getting
somewhere with very detailed agent-based
models — but that was 20 years ago. And
after all this time, it’s all still manifestos and
promises of great things one of these days.”
!
Paul Krugman, Nov. 30, 2010, in response to an article
about INET housing project in WSJ.
4
5. Why
isn’t
ABM
the
mainstay
of
economics?
• Math
culture
is
deeply
rooted
– papers
scored
too
much
on
math
vs.
science
– disdain
and
distrust
of
simula$on
– fascina$on
with
ra$onality
and
op$mality
• ABM
is
a
fringe
ac$vity,
hasn’t
delivered
home
runs
needed
to
enter
establishment
– chicken/egg
problem
• Lucas
cri$que
5
6. Lucas
Cri$que
• Recession
of
70’s.
“Keynesian”
econometric
models.
• Phillips
curve:
Rising
prices
~
rising
employment
• Following
Keynesians,
Fed
inflated
money
supply
• Result:
Inflation,
high
unemployment
=
stagflation
• Problem:
People
can
think
• Conclusion:
Macro
economic
models
must
incorporate
human
reasoning
• Solution:
Dynamic
Stochastic
General
Eq.
models
6
7. Advantages
of
DSGE
• “Micro-‐founded”
(unlike
econometric
models)
– can
be
used
for
policy
analysis.
• Time
series
models
– ini$alizable
in
current
state
of
the
world,
can
make
condi$onal
forecasts
• Describe
a
specific
economy
at
a
specific
$me.
• In
some
sense
parsimonious
7
8. Why
agent-‐based
modeling?
• Diversifies toolkit of economics: Complements DSGE
and econometric models. Also microfounded
• Time is ripe: increased computer power, Big Data,
behavioral knowledge. Never let a crisis go to waste.
• Hasn’t really been tried yet -- crude estimates:
– econometric models: 30,000 person-years
– DSGE models: 20,000 person-years
– agent-based models: 500 person-years
• Successes elsewhere: Traffic, epidemiology, defense
• Examples of successes in economics:
– Endogenous explanations of clustered volatility and heavy
tails; firm size; neighborhood choice
8
9. Advantages
• Can faithfully represent real institutions
• Easily captures instabilities, feedback, nonlinearities,
heterogeneity, network structure,...
• Shocks can be modeled endogenously
• Easy to do policy testing
• Easy to incorporate behavioral knowledge
• Can calibrate modules independently using micro
data -- much stronger test of models!
– In some sense between theory and econometrics
• ABMs synthesize knowledge:
– Possible to understand what is not understood
9
10. Challenges
• Little prior art
• Developing appropriate abstractions
– What to include, what to omit?
– How to keep model simple yet realistic?
• Micro-data to calibrate decision rules?
• Data censoring problems
• Realistic agent-based models are complicated.
• No theoretical foundation
Cautionary tale of weather forecasting
10
11. Formula$ng
decision
rules
• Make
something
up
• Take
from
behavioral
literature
• Perform
experiments
in
context
of
ABM
• Interview
domain
experts
• Calibrate
against
microdata
• Learning
and
selec$on
!
(ABM
can
respond
to
Lucas
cri$que)
11
12. Model
of
leveraged
value
investors
• Collaborators:
Sebas$an
Poledna,
Stefan
Thurner,
John
Geanakoplos
12
14. Defaults
under
diverse
regulatory
regimes
14
1 5 10 15 20
0
0.02
0.04
0.06
0.08
0.1
0.12
max
<probabilityoffundfailure>
(a)
unreg.
basel
perfect. h.
15. Is
it
possible
to
make
a
quan$ta$ve
ABM
that
can
be
used
as
a
$me
series
model?
(and
therefore
can
compete
with
DSGE)
15
16. Housing
model
project
• Senior
collaborators:
Rob
Axtell,
John
Geanakoplos,
Peter
Howik
• Junior
collaborators:
Ernesto
Carella,
Ben
Conlee,
Jon
Goldstein,
Makhew
Hendrey,
Philip
Kalikman
• Funded
by
INET
three
years
ago
for
$375,000.
16
17. Agent-‐based
model
of
housing
market
• Goal:
condi$onal
forecasts
and
policy
analysis
• Simula$on
at
level
of
individual
households
• Exogenous
variables:
demographics,
interest
rates,
lending
policy,
housing
supply.
• Predicted
variables:
prices,
inventory,
default
• 16
Data
sets:
Census,
mortgages
(Core
Logic),tax
returns
(IRS),
real
estate
records
(MLA),
...
• Current
goal:
Model
Washington
DC
metro
area
• Future
goal:
All
metro
areas
in
US
17
18. Module
examples
• Desired
expenditure
model
– buyers’
desired
home
price
as
a
func$on
of
household
income
and
wealth
• Seller’s
pricing
model
– seller’s
offering
price
as
a
func$on
of
home
quality,
$me
on
market,
and
total
inventory
• Buyer-‐seller
matching
algorithm
– links
buyers
and
sellers
to
make
transac$ons
• Household
wealth
dynamics
– models
consump$on
and
savings
• Loan
approval
– qualifies
buyers
for
loans
based
on
income,
wealth;
must
match
issued
mortgages
18
19. Housing
model
algorithm
At
each
$me
step:
• Input
changes
to
exogenous
variables
• Update
state
of
households
– income,
consump$on,
wealth,
foreclosures,
...
• Buyers:
– Who?
Price
range?
Loan
approval,
terms?
• Sellers:
– Who?
Offering
price?
Price
updates?
• Match
buyers
and
sellers
– Compute
transac$ons
and
prices
19
21. • Complete
agent-‐based
model
of
economy
• Agents:
Households,
firms,
banks,
mutual
funds,
central
banks.
Both
financial
and
macro.
• Goals:
–
tool
for
policy
decision
making
– series
of
models
of
increasing
complexity
– create
standard
sopware
library
– Be
useful
for
central
banks
CRISISComplexity Research Initiative
for Systemic InstabilitieS
EUROPEAN
COMMISSION
21
23. Produc$on
sector
• Input-‐output
economy
– firms
are
myopic
profit
maximizers
that
use
heuris$cs
to
set
price
and
quan$ty
of
produc$on
– variable
labor
supply
– finance
produc$on
via
mixture
of
credit
and
equity
– input-‐output
structure
mimicking
real
economy
• For
comparison
have
simpler
alterna$ves,
e.g.
fixed
labor
Cobb
Douglas,
exogenous
dividends.
23
24. Financial
sector
• Banks
– take
deposits
from
firms
and
households,
lend
to
firms,
buy
and
sell
shares,
interbank
market.
– Investment
strategies:
trend
following,
fundamental
Central
bank
– conven$onal
and
unconven$onal
policy
opera$ons
– interest
rate
can
be
formed
endogenously
• Firms
– borrow
from
banks
to
fund
produc$on
• Adding:
mortgages,
shadow
banking,
mutual
funds,
…
24
28. Conclusions
• We
have
lots
of
work
to
do
to
make
models
that
can
seriously
compete
with
DSGE
• Should
be
possible
to
make
model
with
rich
ins$tu$onal
structure,
calibrated
to
real
world
• Capability
to
put
an
economy
in
current
state
of
a
real
economy,
make
condi$onal
forecasts
• Economic
models
of
future
will
be
ABM,
but
when?
• Chicken-‐egg
problem
to
get
ABM
off
the
ground
• Economics
needs
more
diversity!
28
29. Design
philosophy
• As
simple
as
possible
(but
no
more)
• Design
model
around
available
data
• Fit
modules
and
agent
behaviors
independently
from
target
data,
using
several
different
methods:
– micro-‐data
for
calibra$on
and
tes$ng
– consult
domain
experts
for
behavioral
hypotheses
– adap$ve
op$miza$on
to
cope
with
Lucas
cri$que
– economic
experiments
• Systema$cally
explore
model
sensi$vi$es
• Plug
and
play
• Standardized
interfaces
• Industrial
code,
sopware
standards,
open
source
29