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Category: Future Scenarios and Systems Thinking

Research Presentation

Income and Lifespan Inequality

  1. Presentation Slides
  2. Executive Summary
  3. Poster

All reference sources are included in each slide under the speakers notes:

 

Questions and Topics

What will be the ways for technology to increase or decrease the gap among people?

Research Topic:

  • Income and lifespan inequality
    • across different countries
    • and inside a country
  • Areas to look into (from class feedback)
    • pattern of income inequality; when are the moments that the gap got widened or narrowed
    • middle-class and their age
    • how many generations does it take for poor to be rich (economic mobility)?
    • role of education
    • relationship between time and income inequality?

Reference: Hans Rosling’s Talk

The quality of life in overall countries got dramatically elevated, however, with significant amount of inequality. It is visible by how bubbles that represent different countries are located in scattered shape in 2008, compared to 1830.

It gets more interesting when he takes apart some cities in China and starts locating them next to other countries. In 2009, China had relatively lower lifespan and income level than Italy – while Shanghai alone was in comparable status. Then, he takes Guizhou apart and locate next to Pakistan. Furthermore, within Guizhou, the poorest rural area was comparable with Ghana.

 

Trends and Critical Uncertainties

This week’s discussion about a list of trends and critical uncertainties was done with Laura Weinman Kerry, with focus on global migration because it was both Laura’s and my previous topic as well. While Laura researched more details about citizenship and national identity, I looked bit more into the issues with international economic transfer, such as outsourcing and fair trade among countries.

Globalisation and the Economic Transfer

  • Outsourcing
    • Immigration vs. outsourcing: Effects on labor markets: shows either immigration or outsourcing of a labor-intensive fragment of production may serve to raise the wage rate of national labor in a developed country (regarding Anti-globalizers’ concern that they will lower the wage rate). (positive perspective)
    • Skilled Immigration and Economic Growth: Skilled immigrants have achieved great success in founding U.S. engineering and technology startups, which have in turn contributed greatly to the country’s economic growth over time. (positive perspective)
    • Back-sourcing?
    • Outsourcing and financial performance: A negative curvilinear effect: the existence of organisations can be attributed to market failure that induces transaction costs; there are other industry factors such as the need for local responsiveness versus global integration (The Multinational Mission: Balancing Local Demands and Global Vision by C.K. Prahalad, Y.L. Doz); heavy reliance on internal sourcing leads to poor performance, and it is at its worst when firms apply it by default (Domberger, 1998)….Firms that become hollow or virtual lack a solid basis for competing and can neither innovate enough nor learn much (Chesbrough and Teece, 1996; Kotabe, 1998). (neutral~negative perspective)
  • Fair Trade
    • Re-embedding global agriculture: The international organic and fair trade movements: Fair trade aims to ensure that the poorest actors in a supply chain benefit more from the overall financial value creation as a development tool…. large corporations may capture the most lucrative share, threatening the sector’s progressive social and environmental foundations (Buck et al., 1997). (positive perspective)
    • Fair Trade and Harmonization: Prerequisites for Free Trade?, Volume 2: The conflict in each policy area tends to center around complaints by countries with high standards against the countries with low standards….international harmonization has been seen from the beginning not only as a desirable end in itself, but also as a necessary condition to adoption of higher labor standards in any one country. Suggestion: it should be possible for the International Labor Organization (ILO) and General Agreement on Tariffs and Trade (GATT) to agree on the principle that comparative advantage in trade should not be based on the violation of the most fundamental workers’ rights, but not including other more technical labor standards relating to wage differentials and occupational safety and health. (neutral~negative perspective)
    • Black Gold: 2006 documentary film that focuses on the coffee growers of the Oromia Region of southern and western Ethiopia. (positive perspective)

 

Predetermined Elements

This week’s discussion about predetermined elements and current conditions was done with Jiyao Zhang, with focus on demographic phenomenon.

Current Conditions and Predetermined Elements

  • Current Condition: Aging Society & Low Birthrate in developed countries (than underdeveloped countries)
  • Predetermined Elements:
    • Change in intention to have children and active birth control
    • Female labor participation
    • Lower infant fatality rate and longer lifespan

How the Current Conditions Map to the Future

  • Plausible Future Condition: Global migration in bigger scale that influences economic & social systems
  • Current Elements
    • More accessible and exposed to global info, which can result desire of migration and less attachment to nationality
    • Inequality within a country is higher than the one across countries
    • Gap is going to be bigger and bigger
    • Example: Investor visa; money = power to achieve desire
    • Example: Refugee Crisis, Chinese and Korean people who spend money overseas
    • If domestic opportunities grow, less migration? (Japan)
    • Developed countries become more active about accepting migrants due to their aging society and low birthrate, while natives’ preference doesn’t match their idea.

Example of bad predetermination:

  • An Essay on the Principle of Population by Thomas Robert Malthus
    • Malthusian Trap
    • Published in 1798
    • Population increases yet food production doesn’t
    • Totalitarianism: decrease of low-income population to avoid overpopulation
    • Main problem: interpreting economy system only in biological aspects without considering industrial revolution and improvement in agriculture technology.

 

Increasingly, inequality within, not across, countries is rising

Inequality and development across and within countries

 

Driving Forces: Machine Learning / AI

Notes from a discussion of machine learning as a driving force with Andrew Lee. We discussed machine learning as a driving force of:

  1. The shift from products to services
  2. Increasing distrust of the media

Potential of Machine Learning / AI

  • Origins as a driving force:
    • Ability to solve problems that are difficult to specify or define
  • Historical precursors:
    • Gaming industry created cheap GPU’s
    • Mature tech companies with web-scale data sets
    • Foundational concepts such as back propagation
  • How does it influence the conditions which are influenced by it?
    • The shift from products to services
      • Companies have an incentive to create service platforms to collect data and collect as much data as possible
    • Distrust of media
      • Machine learning can synthesize media that is difficult for humans to detect as machine generated
      • Filters run by machine learning algorithms sometimes promote low quality information. eg facebook filtered feed, google suggested answers
      • Personalization can feel obtuse or “creepy”
  • What are the counter-forces at play?
    • Privacy regulation
    • Distrust of corporations
    • Fear of artificial intelligence and black box algorithms
    • Hype cycles / AI winters
    • Platform ecosystem stakeholders with competing interests. eg iOS not allowing certain tracking
  • Research on probability and random algorithms is the most popular field
  • Because such things as chess are no longer considered to be in the field of AI, but rather a better and faster performance of calling data.
  • In other words, they don’t require complex thinking process.
  • Although the definition of “intelligence” is still debatable, AI is approached in different direction.
  • Solving requests that require the understanding of contexts
  • Mathematics and statistics creating categories
  • Brain Simulation: the concept and scientific project of creating a computer-run model of brain neuron connections
  • Bottom-up approach: the piecing together of systems to give rise to more complex systems, thus making the original systems sub-systems of the emergent system (A top-down approach is essentially the breaking down of a system to gain insight into its compositional sub-systems in a reverse engineering fashion)
  • How to Create a Mind by Ray Kurzweil