How does the 21st century charity work? The case of Kiva.org

9

October

2017

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Bank loans allow people to follow their dream and try to change their social status. The expression ‘money brings more money’ is a simple way to describe a much more complex phenomenon. If you can afford to open an activity of whatever kind you can higher the likelihood that you can change the magnitude of your belonging since you can have a larger income day after day, which can be further invested in other activities. But if you don’t have the money in the first place, you can’t really afford to get into this virtuous spiral.

To solve this scenario, Flannery in 2005 decided to open Kiva (Fannery, 2007), a platform that is described by Mittelman and Rojas-Mendez (2013) as Online Social Lending for Development (OSL4D). Basically, on these platforms users are allowed to back entrepreneurial projects from less developed countries, allowing people to have a chance in doing the step up in the social ladder. All the loans are interest free but, in contrast to how traditional charity usually works, the money has to be repaid by the entrepreneur, so that it can be ‘reinvested’ in a new project.

To ensure the fact that the entrepreneurs will be able to repay the project and that, thus, their idea is feasible, Kiva decided to opt for a local filtering, the so-called Field Partners. Due to the fact that they are charging entrepreneurs on the borrowings, at first glance they appear to be unfitted in a system where the 0% interest rate is the rule. However, keeping the external loans with the form of grants would have attracted a discrete number of opportunistic behaviors too. With the implementation of field partners in the system, Kiva has raised the price of the loan, factor that nudged out of the market the inefficient entrepreneurs, realizing the quality cream off. This happened since in microfinance, as theorized by Ghosh and Van Tassel (2012), the side that is searching for a profit is going to shrink as the cost of the transaction increases. Moreover, this filter was also established to reduce the number of entrepreneurs. Ly and Mason (2011) concluded that an increase in the number of entrepreneurs (competitors) has an adverse effect on the project funding speed, one of the strong points of Kiva.

The exponential growth and the 95+% of repaid loans of Kiva is a signal of the fact that this structure works efficiently and that the system works both for the entrepreneurs and for the lenders.

Are you ready to be part of this social evolution? If you do just go to Kiva (https://www.kiva.org/) and support your first entrepreneur!

References:

Flannerly, M. (2007), “Kiva and the Birth of Person-to Person Microfinance”, Innovations: Technology, Governance & Globalization, Winter/Spring 2007, 2:1-2, 31-56
Mittelman, R. and Rojas-Méndez, J.I. (2013), “Exploring Consumer’s Needs and Motivations in Online Social Lending for Development”, Journal of Nonprofit & Public Sector Marketing, 25:4, 309-333

Ghosh, S. and Van Tassel, E. (2012), “Funding microfinance under asymmetric information”, Journal of Development Economics, 101, 8–15
Ly, P. and Mason, G. (2011), “Competition between microfinance NGOs: Evidence from Kiva”, World Development, 40:3.

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Is a videogame AI already better than a professional player? Yes.

21

September

2017

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With the term, Artificial Intelligence (AI) usually we describe the ability of a Machine to do functions, reasoning and having a behavior that is typical of a human brain. AI was at first described by McCarthy during a famous interdisciplinary seminar happen in 1956 in New Hampshire. As Marvin Minsky, one of the AI pioneers, described, the scope of this new discipline was to allow machine to do processes and actions that would require intelligence in case a human had to do them. The very final objective of such technology was to create an artificial brain able to emulate a human brain up to the point that it would be undistinguishable from a human one.

 

Games at first (and videogames subsequently) have been identified as a fertile ground on which AI could be applied and could try to evolve. This is mostly because the environment is fairly simple and limited, with a small number of parameters and variables that have to be observed and taken into consideration for the decision making.

 

The first prototypes of videogames were created in the late ’40s, but only in the early ’50s the first videogames came out in the market. In 1952 A.S. Douglas while working on his thesis at Cambridge created OXO, a graphic version of tic-tac-toe, in which the opponent of a human player was an artificial intelligence player. However, only during the ‘70s, with Atari’s PONG, videogames reached the mass and became a popular pastime. This videogame was a simplification of ping-pong, with two bars, one at each side, that could be moved up and down, trying to hit a bouncing ball and shooting it towards the other side of display. This game could be played both by due human players facing each other as well as a single player (facing therefore the AI). In this case, AI will play the game as a human player, trying to move the light bar in the position in which the ball was expected to land. Given the fact that the ball direction was computed by the game as well, many times the AI would have end up cheating, since it knew precisely were the ball would have landed.

 

Over the years, videogames, as well as the AI, became profoundly different, with graphics and gameplay that emulate the reality so much that they are used for training in many scenarios (e.g. Formula 1). The AI in particular tries to play the game as efficiently as possible, using human behaviors and avoiding standardized actions.

 

For instance, Real Time Strategy games (RTS), as the well-known Age of Empires, the AI proved to be able to create every time different games although the macro-variables (map, civilizations, difficulty and number of players) are the same. This is possible since each AI player has to manage its initial resources and accumulate even greater amounts so that it can support the war effort required by the game, it must be able to form and manage its army, defending strategically important positions such as its extraction and production facilities and systematically attacking the enemy. This bundle of tasks can be done in different ways and with different strategies, factors that will create very different outcomes of the same game. However, these AI could not beat in a match professional (or just highly skilled) players, since no matter how complex their behavior might be, a human brain will be always able to find a way to prevail thanks to tricks and triggers that will lead the AI to an inefficient decision.

 

Yet, the latest development of AI is incredibly powerful. Thanks to Machine Learning technology and Artificial Neural Network (Adversial Neural Network to be precise), Elon Musk’s OpenAI Bot (https://openai.com/the-international/) after only 2 weeks of learning was the first AI that was able to beat professional players in a complex game as DOTA2, a Multiplayer Online Battle Arena, without AI cheating involved. All the top players of the Internationals (the “World Cup” of such game) challenged the bot in a 1-vs-1 match, but none of them was able to overtake it. Surprisingly although they are the best players in the world, the bot was so powerful that some games that were not even tight matches. The result was so surprising that the developing team decided to allocate a prize of 1.000$ for anyone able to beat such bot. It is no surprise however the fact that no player was yet able to win such prize.

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Is any of you going to accept the challenge?

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