Blainey, Geoffrey. 1973. The Causes of War. New York: Free Press.
This book is a wide ranging exploration of the causes of war and peace. The author refers to its structure as an "intellectual detective story" (vii), and proceeds to explore and adjudicate a large chunk of extant literature, keeping some insights, and tossing others away.
The book begins with a claim that, "for every thousand pages published on the causes of wars there is less than one page directly on the causes of peace" (3). Many of the theories explored in the first two chapters are arcane, and are not explorations of the causal dynamics of international conflict, but rather the identification of a certain possibly correlated variable (ie., there is a certain amount of energy that a society has, and it can be for international conflict or domestic, or that this energy can be used in building the economy, or that there is a process of jingoism, war and then weariness, which eventually leads to more war (87), etc).
Theories of war explore, in part, perceptions about the likelihood of victory, finding that many who perceive wars to be winnable and short are likely to engage. Though, when this happens from two sides, it becomes problematic, as both sides cannot win.
"A prediction of a war about to be fought is thus a crystallization of many moods and arguments, each of which has some influence on the decision to make war" (54).
Potential alliances also inform the decision to go to war.
There was previously a relationship between wars and the deaths/succession of monarchs.
Few wars are started in December and January.
The discussion of balance of power is interesting. Is it the balancing of power that makes for more peace, or is it the hegemon that makes for a peaceful environment? This is a crucial question in the study of international conflict. "The idea that an even distribution of power promotes peace has gained strength partly because it has never been accompanied by tangible evidence. Like a ghost it has not been captured and examined for pallor and pulsebeat" (112).
On the interaction of diplomats: "In peace time the relations between two diplomats are like relations between two merchants. While the merchants trade in copper or transistors, the diplomats' transactions involve a variety of other issues which they have in common. A foreign minister or diplomat is a merchant who bargains on behalf of his country. He is both buyer and seller, though he buys and sells privileges and obligations rather than commodities. The treaties he signs are simply more courteous versions of commercial contracts"
(115).
"Wars usually end when the fighting nations agree on their relative strength, and wars usually begin when fighting nations disagree on their relative strength" (122).
“One may suggest that nations, in assessing their relative strength, were influenced by seven main factors: military strength and the ability to apply that strength efficiently in the chosen zone of war; predictions of how outside nations would behave in the event of war; perceptions of internal unity and of the unity or discord of the enemy; memory or forgetfulness of the realities and sufferings of war; perceptions of prosperity and of ability to sustain, economically, the kind of war envisaged; national and ideology; and the personality and mental qualities of the leaders who weigh the evidence and decide for peace or war” (123).
Skipped book 3.
Monday, November 16, 2009
Wednesday, September 16, 2009
Singer: Correlates of War: I
Singer, JD. 1978. The Correlates of War. New York: Free Press.
The Correlates of War project has provided much literature and remains a foundational cut in the study of global conflict. Singer is obviously the god-father of this project, and, while it has found many detractors, it has undoubtedly moved the debate about state instability and war in new directions.
Volume one of the series represents Singer's own work, primarily. It begins by exploring some of the potential cause of war, such as arms build-up, and the logic behind such transactions. It explores issues widely, arguing for the normative study of conflict following a scientific method. It is also adequately, in my opinion, cautious about the veracity of what can be known in the field of conflict, as well as more broadly within the social sciences; the projects is intentionally titled the "correlates" and not the "causes".
When he moves into discussions of prediction, he argues for more thorough use of computer simulations in place of games, intuition and other methods where assumptions are not made as explicit as possible. Through computer simulations, scholars must make the variables, parameters and theories used in their models explicit. This is seen as being valuable.
The experiments in this book are directed at wars between nations. Some basic and standard statistical methods are used to explore bivariate and multivariate relationships between independent variables as they explain the output of war.
Much of the book reads pedantically.
The Correlates of War project has provided much literature and remains a foundational cut in the study of global conflict. Singer is obviously the god-father of this project, and, while it has found many detractors, it has undoubtedly moved the debate about state instability and war in new directions.
Volume one of the series represents Singer's own work, primarily. It begins by exploring some of the potential cause of war, such as arms build-up, and the logic behind such transactions. It explores issues widely, arguing for the normative study of conflict following a scientific method. It is also adequately, in my opinion, cautious about the veracity of what can be known in the field of conflict, as well as more broadly within the social sciences; the projects is intentionally titled the "correlates" and not the "causes".
When he moves into discussions of prediction, he argues for more thorough use of computer simulations in place of games, intuition and other methods where assumptions are not made as explicit as possible. Through computer simulations, scholars must make the variables, parameters and theories used in their models explicit. This is seen as being valuable.
The experiments in this book are directed at wars between nations. Some basic and standard statistical methods are used to explore bivariate and multivariate relationships between independent variables as they explain the output of war.
Much of the book reads pedantically.
Wednesday, August 19, 2009
The Economic Benefits of Intelligent Technologies
Access Economics Pty Limited. 2009. The Economic Benefits of Intelligent Technologies. April.
"This report reviews the potential economic benefits that may flow from the adoption of smart technologies and systems in different parts of the economy...The amount of all forms of data that are regularly collected throughout the community rapidly expand on a daily basis. Intelligent systems have the capacity of using these data to improve decision-making and co-ordination throughout society, thereby lifting economic efficiency and living standards" (i).
"Overall, the adoption of smart technologies and systems in the five areas identified are conservatively estimated to result in:
• an increase in the net present value (NPV) of Gross Domestic Product...of between $35 billion to $80 billion over the first ten years, with precise estimates depending on how much spare capacity is in the economy;
• in the case where the economy is operating at full employment, an increase of labour productivity of around 0.5% as the deployment of the technologies becomes widespread; and
• in the case where the economy is operating at less than full employment the impact on jobs is more pronounced as the technologies become widespread. In 2014 alone this results in more than 70,000 jobs being added to the economy" (ii).
This report explores different areas in which smart technology can have profound effects, specifically electricity, transport, health, water and high-speed broadband (HSBB).
"The rollout of high speed broadband both enables the more effective implementation of smart technologies in other areas of the economy-including electricity, water, transport and health-as well as providing other direct and substantive benefits to individuals, businesses and the environment through the availability to deliver a range of commercial, government and information services more efficiently" (1).
The report reviews the literature on the topic, and then explores smart-technologies in an equilibrium model. Two scenarios are then explored: an economy at full employment and an economy with less than full employment.
"This report reviews the potential economic benefits that may flow from the adoption of smart technologies and systems in different parts of the economy...The amount of all forms of data that are regularly collected throughout the community rapidly expand on a daily basis. Intelligent systems have the capacity of using these data to improve decision-making and co-ordination throughout society, thereby lifting economic efficiency and living standards" (i).
"Overall, the adoption of smart technologies and systems in the five areas identified are conservatively estimated to result in:
• an increase in the net present value (NPV) of Gross Domestic Product...of between $35 billion to $80 billion over the first ten years, with precise estimates depending on how much spare capacity is in the economy;
• in the case where the economy is operating at full employment, an increase of labour productivity of around 0.5% as the deployment of the technologies becomes widespread; and
• in the case where the economy is operating at less than full employment the impact on jobs is more pronounced as the technologies become widespread. In 2014 alone this results in more than 70,000 jobs being added to the economy" (ii).
This report explores different areas in which smart technology can have profound effects, specifically electricity, transport, health, water and high-speed broadband (HSBB).
"The rollout of high speed broadband both enables the more effective implementation of smart technologies in other areas of the economy-including electricity, water, transport and health-as well as providing other direct and substantive benefits to individuals, businesses and the environment through the availability to deliver a range of commercial, government and information services more efficiently" (1).
The report reviews the literature on the topic, and then explores smart-technologies in an equilibrium model. Two scenarios are then explored: an economy at full employment and an economy with less than full employment.
Tuesday, August 4, 2009
Transforming America's Power Industry
The Brattle Group. Transforming America's Power Industry: The Investment Challenge 2010-2030.
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
Friday, July 31, 2009
Hledik: How Green Is the Smart Grid?
Hledik, R. 2009. How Green Is the Smart Grid? The Electricity Journal 22, no. 3: 29-41.
"A simulation of the US power system suggests that both conservative and more technologically aggressive implementations of a smart grid would produce a significant reduction in power sector carbon emissions at the national level. A conservative approach could reduce annual CO2 emissions by 5 percent by 2030, while the more aggressive approach could lead to a reduction of nearly 16 percent by 2030" (29).
Two scenarios are examined, as the concept of "smart grid" has not been fully concretized. Scenario one explores technologies that are currently available. The second explores an expansion of possible future technologies.
"At a basic level, the smart grid will serve as the information technology backbone that enables widespread penetration of new technologies that today's electrical grid cannot support. These new technologies include cutting-edge advancements in metering, transmission, distribution, and electricity storage technology, as well as providing new information and flexibility to both consumers and providers of electricity. Ultimately, access to this information will improve the products and services that are offered to consumers, leading to more efficient consumption and provision of electricity" (30).
A key aspect of smart grid technology is advanced metering infrastructure (AMI). This leads to dynamic pricing of energy.
There are a variety of pushes to produce more energy from renewable sources. These each have different goals. Doubling the Renewable Portfolio Standards (RPS) would lead to 19% of energy being produced by renewables in 2030. T. Boone Pickens calls for 20% from renewable by 2020, as does the EU. The New Apollo program wants 25% by 2025. Google's energy plan calls for 60% by 2030. Repower America suggests that 75% come from renewables in the next 10 years.
The Brattle Group's RECAP model is used to explore these scenarios using EIA data and assumptions.
"Among the key outputs of RECAP is a forecast of the CO2 emissions from both existing and new power plants. This forecast depends on both the projected mix of new plants that will be added to the system, and the operation of all power plants that are connected to the grid. Implementation of a smart grid will influence both of these. In this study, there are four specific smart grid impacts that have been modeled" (37). These are: peak demand reduction, conservation, increased penetration of renewables and reduced line loss.
Three scenarios are explored out to 2030. Firstly, a business as usual forecast, which shows average annual increases in CO2 emissions of 0.7%. Secondly, the Conservative Scenario, which posits the effects of existing ICT technology on CO2 shows an average annual growth of 0.5%. Finally, the Expanded Scenario shows an average annual growth rate of -0.1%.
Overall, dynamic pricing leads to a nominal improvement in overall CO2 emissions. The combination of dynamic pricing and information displays leads to a 5% reduction in annual CO2 emissions. "By far, the single largest reduction comes from the cleaner mix of generating capacity that is enabled by distributed resources and an expanded transmission system, amounting to a 9.9% reduction in CO2 emissions" (39).
"A simulation of the US power system suggests that both conservative and more technologically aggressive implementations of a smart grid would produce a significant reduction in power sector carbon emissions at the national level. A conservative approach could reduce annual CO2 emissions by 5 percent by 2030, while the more aggressive approach could lead to a reduction of nearly 16 percent by 2030" (29).
Two scenarios are examined, as the concept of "smart grid" has not been fully concretized. Scenario one explores technologies that are currently available. The second explores an expansion of possible future technologies.
"At a basic level, the smart grid will serve as the information technology backbone that enables widespread penetration of new technologies that today's electrical grid cannot support. These new technologies include cutting-edge advancements in metering, transmission, distribution, and electricity storage technology, as well as providing new information and flexibility to both consumers and providers of electricity. Ultimately, access to this information will improve the products and services that are offered to consumers, leading to more efficient consumption and provision of electricity" (30).
A key aspect of smart grid technology is advanced metering infrastructure (AMI). This leads to dynamic pricing of energy.
There are a variety of pushes to produce more energy from renewable sources. These each have different goals. Doubling the Renewable Portfolio Standards (RPS) would lead to 19% of energy being produced by renewables in 2030. T. Boone Pickens calls for 20% from renewable by 2020, as does the EU. The New Apollo program wants 25% by 2025. Google's energy plan calls for 60% by 2030. Repower America suggests that 75% come from renewables in the next 10 years.
The Brattle Group's RECAP model is used to explore these scenarios using EIA data and assumptions.
"Among the key outputs of RECAP is a forecast of the CO2 emissions from both existing and new power plants. This forecast depends on both the projected mix of new plants that will be added to the system, and the operation of all power plants that are connected to the grid. Implementation of a smart grid will influence both of these. In this study, there are four specific smart grid impacts that have been modeled" (37). These are: peak demand reduction, conservation, increased penetration of renewables and reduced line loss.
Three scenarios are explored out to 2030. Firstly, a business as usual forecast, which shows average annual increases in CO2 emissions of 0.7%. Secondly, the Conservative Scenario, which posits the effects of existing ICT technology on CO2 shows an average annual growth of 0.5%. Finally, the Expanded Scenario shows an average annual growth rate of -0.1%.
Overall, dynamic pricing leads to a nominal improvement in overall CO2 emissions. The combination of dynamic pricing and information displays leads to a 5% reduction in annual CO2 emissions. "By far, the single largest reduction comes from the cleaner mix of generating capacity that is enabled by distributed resources and an expanded transmission system, amounting to a 9.9% reduction in CO2 emissions" (39).
Wednesday, July 15, 2009
Gerner and Schrodt: The Effects of Media Coverage on Crisis Assessment
Gerner, DJ, and PA Schrodt. 1998. The effects of media coverage on crisis assessment and early warning in the Middle East. Early Warning and Early Response.
Media coverage is not uniform, and many point to phenomena such as "media fatigue" to highlight the unevenness of reporting. This piece explores these questions by specifically looking at the Arab-Israeli conflict. It explores media fatigue, finding that it is in fact measurable.
Media fatigue occurs when conflict events are not as heavily covered in regions where conflict events are more likely to occur; as conflicts continue for long periods of time, the media does not report as frequently as when conflicts occur on a more stochastic time horizon. This piece argues that large scale news sources are useful, but that they are more prone to media fatigue. For example, instead of just relying on The NYT for event coding, researches must also look to more specific news sources that deal more clearly with the conflict at hand.
Additionally, media fatigue is not entirely a product of boredom, but also may reflect a kind of competition between different events. For example, the article argues that coverage of the Israeli-Arab conflict was overwhelmed by the collapse of the Soviet Union in 1989.
In the end, however, exploring media fatigue is quite difficult, as it requires counterfactuals.
Media coverage is not uniform, and many point to phenomena such as "media fatigue" to highlight the unevenness of reporting. This piece explores these questions by specifically looking at the Arab-Israeli conflict. It explores media fatigue, finding that it is in fact measurable.
Media fatigue occurs when conflict events are not as heavily covered in regions where conflict events are more likely to occur; as conflicts continue for long periods of time, the media does not report as frequently as when conflicts occur on a more stochastic time horizon. This piece argues that large scale news sources are useful, but that they are more prone to media fatigue. For example, instead of just relying on The NYT for event coding, researches must also look to more specific news sources that deal more clearly with the conflict at hand.
Additionally, media fatigue is not entirely a product of boredom, but also may reflect a kind of competition between different events. For example, the article argues that coverage of the Israeli-Arab conflict was overwhelmed by the collapse of the Soviet Union in 1989.
In the end, however, exploring media fatigue is quite difficult, as it requires counterfactuals.
Schrodt: Event Data in Foreign Policy Analysis
Schrodt, PA. 1994. Event data in foreign policy analysis. Foreign Policy Analysis: Continuity and Change. Prentice-Hall: 145-166.
"Event data are a formal method of measuring the phenomena that contribute to foreign policy perceptions. Event data are generated by examining thousands of newspaper reports on the day to day interactions of nation-states and assigning each reported interaction a numerical score or categorical code. For example, if two countries sign a trade agreement, that interaction might be assigned a numerical score of +5, whereas if the two countries broke off diplomatic relations, that would be assigned a numerical score of -8. When these reports are averaged over time, they provide a rough indication of the level of cooperation and conflict between the two states" (2).
Creating event data involves three distinct steps: 1. identify sources; 2. develop a coding system; 3. train human coders.
"Event data was originally developed by Charles McClelland in the early 1960s as a bridge between the traditional approach of diplomatic history and the new quantitative analysis of international politics advocated in the behavioral approach. McClelland reasoned that history could be decomposed into a sequence of discrete events such as consultations, threats, promises, acts of violence and so forth. Event data formed the link between the then-prevalent general systems theories of international behavior and the textual histories which provided an empirical basis for understanding that behavior" (7).
"Event data are a formal method of measuring the phenomena that contribute to foreign policy perceptions. Event data are generated by examining thousands of newspaper reports on the day to day interactions of nation-states and assigning each reported interaction a numerical score or categorical code. For example, if two countries sign a trade agreement, that interaction might be assigned a numerical score of +5, whereas if the two countries broke off diplomatic relations, that would be assigned a numerical score of -8. When these reports are averaged over time, they provide a rough indication of the level of cooperation and conflict between the two states" (2).
Creating event data involves three distinct steps: 1. identify sources; 2. develop a coding system; 3. train human coders.
"Event data was originally developed by Charles McClelland in the early 1960s as a bridge between the traditional approach of diplomatic history and the new quantitative analysis of international politics advocated in the behavioral approach. McClelland reasoned that history could be decomposed into a sequence of discrete events such as consultations, threats, promises, acts of violence and so forth. Event data formed the link between the then-prevalent general systems theories of international behavior and the textual histories which provided an empirical basis for understanding that behavior" (7).
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