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---
title: "Power and the Realisms"
subtitle: POSC 3610 -- International Conflict
author: Steven V. Miller
institute: Department of Political Science
titlegraphic: /Dropbox/teaching/clemson-academic.png
date:
fontsize: 10pt
output:
beamer_presentation:
template: ~/Dropbox/miscelanea/svm-r-markdown-templates/svm-latex-beamer.tex
latex_engine: xelatex
dev: cairo_pdf
fig_caption: true
slide_level: 3
make149: true
mainfont: "Open Sans"
titlefont: "Titillium Web"
---
```{r setup, include=FALSE, cache=F, message=F, warning=F, results="hide"}
knitr::opts_chunk$set(cache=TRUE, warning=F)
knitr::opts_chunk$set(fig.path='figs/')
knitr::opts_chunk$set(cache.path='cache/')
knitr::opts_chunk$set(
fig.process = function(x) {
x2 = sub('-\\d+([.][a-z]+)$', '\\1', x)
if (file.rename(x, x2)) x2 else x
}
)
```
```{r loadstuff, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
options(knitr.kable.NA = '')
library(car)
library(tidyverse)
library(janitor)
library(knitr)
library(stevemisc)
```
```{r loaddata, cache=T, eval=T, echo=F, message=F, error=F, warning=F}
# ICOWactive <- read.csv("~/Dropbox/data/icow/icow-provisional-1.01/200199.csv") %>% tbl_df()
CINC <- read.csv("~/Dropbox/data/cow/cinc/NMC_5_0.csv") %>% tbl_df()
```
# Introduction
### Goal for Today
*Discuss power as structural property and the various realism paradigms surrounding it.*
### What is Power?
Two conceptualizations of power focus on:
- relations
- resources
### Power as Relational
Common argument is that power is some kind of coercion.
- i.e. the ability to get someone else to do what they would otherwise not do.
Various aspects to power in this framework.
- Persuasion
- Rewards
- Punishments
- Coercion
- Generally: force-price-legitimacy framework
### Problems With This Interpretation
Several problems follow this concept of power for our purposes.
- Counterfactuals are hard
- Unobservables
- Attribution
### Power As Resources
More common interpretation in IR: power is resources.
- Major advantage: not conflating "power" (i.e. the cause) with outcomes we want to study (i.e. the effect)
# Power, Realism, and its Flaws
## Measuring Power
### Elements of Power
Any number of ways of measuring power (e.g. (in)tangible, observable/latent). Practically we go for:
- Terrain
- Natural resources (e.g. oil)
- Industrial capacity
- Military quality/preparedness
- Population
- Wealth (latent)
- National character (largely unobservable/stereotypes)
### Measuring Power
CoW's National Military Capabilities (NMC) data offer a crude measure of this concept of power.
$$
CINC_{it} = \frac{tpr_{it} + upr_{it} + ispr_{it} + ecr_{it} + mer_{it} + mpr_{it}}{6}
$$
..where:
- $tpr_{it}$ = total population ratio of country *i* in year *t*
- $upr_{it}$ = total urban population ratio of country *i* in year *t*
- $ispr_{it}$ = iron and steel production ratio of country *i* in year *t*
- $ecr_{it}$ = primary energy consumption ratio of country *i* in year *t*
- $mer_{it}$ = military expenditure ratio of country *i* in year *t*
- $mpr_{it}$ = military personnel ratio of country *i* in year *t*
###
```{r cinc-scores-usa-ukg-gmy-rus-1816-2010, echo=F, eval=T, fig.width = 14, fig.height = 8.5}
CINC %>%
mutate(ccode = ifelse(ccode == 260, 255, ccode)) %>%
filter(ccode == 2 | ccode == 200 | ccode == 255 | ccode == 365) %>%
ggplot(., aes(year, cinc, group=factor(ccode), color=factor(ccode))) + geom_line(size=1.5) +
theme_steve_web() +
scale_x_continuous(breaks=seq(1820, 2010, by=10)) +
xlab("Year") + ylab("CINC Score") +
labs(color = "Country",
subtitle="The U.S. has long been the most powerful country in the world, but notice the various power transitions.",
title="CINC Scores for the U.S., UK, Germany and Russia, 1816-2010",
caption="Source: Correlates of War National Military Capabilities Data (v. 5.0)") +
scale_colour_discrete(name="Country",
labels=c("United States", "United Kingdom", "Germany/GFR",
"Russia/USSR")) +
theme(legend.position = "bottom")
```
## The Realisms
### Power as Structural Cause
We focus on the distribution of power in the international system because long-running paradigms are built around it.
### Classical Realism
Drawn from Hans Morgenthau's *Politics Among Nations*.
- Heavily inspired by Thomas Hobbes' *Leviathan*.
- Anarchy reduces "Man" to his "nature".
- The state, *viz*, "Man" is hardwired to will for power.
- End result: bellum omnium contra omnes (war of all against all)
The state (i.e. "Man") pursues power to dominate his rivals.
- Nothing can be done to avoid this.
### Neorealism
Neorealism (aka "structural realism") remains the most prominent approach in security studies. The argument:
- The *structure* of the international system, not "human nature", forces states to pursue power.
- Anarchy has a single logic that forces a state to see means to protect itself.
- Power is the *means*, not the end.
### Neorealism's Assumptions
Neorealism is built on a few core assumptions (think: parsimony).
1. The international system is anarchic.
2. All states possess some type of offensive military capability.
3. States can never be 100% certain of other states' offensive intentions.
4. States are motivated to *survive*.
5. States are rational/strategic actors.
These assumptions will differ slightly from argument to argument.
- They actually come from Mearsheimer (2001).
- Most neorealist scholarship has done a poor job outlining its assumptions, as we shall see.
### Neorealism's Main Conclusions
All told, these assumptions imply states seek a **balance of power** in the international system.
- States eventually fear each other.
- This fear can never be inconsequential.
- International politics becomes a self-help world under anarchy.
- Power becomes the means to security.
Power-seeking leads to the famous problem of the **security dilemma**.
### Neorealism's Hypotheses
Several hypotheses follow these arguments.
- Bipolar systems are more stable than multipolar systems.
- States engage in balancing behavior, such that power distributions converge on a balance.
- States mimic, or echo, one another's behavior.
As we will see, these explanations are flawed in multiple ways.
- The assumptions do not logically imply the hypotheses.
- The empirical record does not vindicate the hypotheses.
## Bipolarity and Stability
### Bipolarity and Stability
Polarity constitutes possibly *the* core argument of neorealism:
- Bipolarity: peace
- Reasons: certainty
- Multipolarity: war
- Reasons: uncertainty.
- More specifically: **buck-passing** and **chain-ganging**
International system was multipolar before the Cold War
- The period saw multiple systemic wars dating back to 1648.
- Cold War was only point in history in which the two largest powers did not (directly) fight each other.
### Problems with the Polarity-Stability Hypothesis
- Not implied by any of the assumptions
- There was nothing special about the "long peace."
### The Hypotheses Do Not Follow the Assumptions
By itself, neorealism's assumptions do not imply the relationship between polarity and stability.
- i.e. "certainty" may embolden risk-taking, "uncertainty" may foster risk-aversion.
- We'd have to add another assumption: all states are equally risk-averse in the face of certainty.
If we relax this even a little bit, we've violated core assumptions of neorealism.
- Violates the unitary actor assumption
- Reduces hypothesized effect of polarity on stability to zero.
- States no longer mimic each other.
### The Polarity-Stability Relationship
Consider a world with A and B in which there are 300 units of "power".
- A: 150
- B: 150
Such a bipolar system would be stable.
- Neither A nor B could destroy each other.
### The Polarity-Stability Relationship
Consider a different world with A and B with 300 units of power.
- A: 151
- B: 149
Neorealism assumes this should be stable, but A could destroy B.
- Only when power is perfectly balanced does bipolarity produce peace.
Objection: power is balanced "enough".
- However, this would deny neorealism's own claim. Bipolarity is supposed to reduce uncertainty!
### The Polarity-Stability Relationship
Consider a five-country system as follows (with 300 units of power).
- A: 75
- B: 74
- C: 75
- D: 74
- E: 2
This system is incidentally stable.
- No one can be eliminated, not even E.
### Bipolarity, Uncertainty, and Stability
Can we salvage the bipolarity-stability argument if we relax the "uncertainty" claim?
- After all, our simple example may not do justice to understanding the real world.
Assume A thinks there's chance *p* it could eliminate B.
- *p* = A's resources/(B's resources + A's resources)
A does not attack B if:
\[ p(U_{AW}) + (1 - p)(U_{AL}) < U_{ASQ} \]
...where $U_{AW}$ = utility for A winning and $U_{AL}$ = utility for A losing.
### Bipolarity, Uncertainty, and Stability
Assume $U_{AW}$ = 1 and $U_{AL}$ = 0. When would A attack B?
\begin{eqnarray}
p(U_{AW}) + (1 - p)(U_{AL}) &>& U_{ASQ} \nonumber \\
pU_{AW} + U_{AL} - pU_{AL} &>& U_{ASQ} \nonumber \\
pU_{AW} - pU_{AL} &>& U_{ASQ} - U_{AL} \nonumber \\
p &>& \frac{U_{ASQ} - U_{AL}}{U_{AW} - U_{AL}} \nonumber \\
p &>& \frac{U_{ASQ} - 0}{1 - 0} \nonumber \\
p &>& U_{ASQ} \nonumber
\end{eqnarray}
A attacks B if the probability of winning is greater than A's utility of the status quo.
### Bipolarity, Uncertainty, and Stability
Assume a world of 300 units of power.
- A: 60
- B: 240
When would A attack B?
- $p = \frac{60}{60+240} = .2$
- If A is really dissatisfied with the status quo (i.e. $U_{ASQ} < .2$), it'll attack B.
This is intuitive but it violates a neorealist assumption of security-oriented behavior!
###
```{r stability-international-systems-1492-1990, echo=F, eval=T, fig.width = 14, fig.height = 8.5}
tibble(
year = c(1492, 1519, 1556, 1609, 1617, 1699, 1713, 1721, 1740, 1808,
1861, 1898, 1905, 1918, 1943, 1945, 1949),
length = c(1519-1492,
1556-1519,
1609-1556,
1617-1609,
1699-1617,
1713-1699,
1721-1713,
1740-1721,
1808-1740,
1861-1808,
1898-1861,
1905-1898,
1918-1905,
1943-1918,
1945-1943,
1949-1945,
1990-1949)
) %>%
ggplot(.,aes(as.factor(year), length)) +
geom_bar(stat="identity", alpha=0.8, color="black", fill="#619cff") +
theme_steve_web() +
geom_text(aes(label=length), vjust=-.5, colour="black",
position=position_dodge(.9), size=4, family="Open Sans") +
xlab("Year of Change in List of Major Powers") + ylab("Duration (in Years) of Era") +
ggtitle("Stability of International Systems (1492-1990)") +
labs(subtitle="i.e. there is nothing special about the ''Long Peace'' of the Cold War.",
caption = "Source: Bueno de Mesquita (2010). Note: 'Stability' defined as a change in the composition of major powers.")
```
# Conclusion
### Conclusion
We study power because we believes its distribution matters to war and peace.
- For our purposes, better to focus on resources than relational power.
Neorealism purports to be a parsimonious explanation of international politics.
- It's also the most common approach in security studies.
However, neorealism suffers from major flaws.
- The assumptions do not imply the hypotheses.
- The hypotheses, however derived, are not supported by the empirical record.