Economists often highlight a gross domestic product (GDP) as a key metric in determining war outcomes, despite historical exceptions, such as the Taliban’s victories over the Soviet and U.S. armies in Afghanistan—nations with vastly superior GDPs. Two critical factors that remain underexplored are the soldier’s willingness to sacrifice for their country and a country’s willingness to risk nuclear war. To address this gap, we conducted a worldwide survey to assess the maximum acceptable level of losses respondents would tolerate in their own country for varying levels of enemy losses. The findings were surprising: respondents, on average, considered 23% casualties (with a median of 10%) as an acceptable loss if it meant 100% destruction of the enemy. To determine which nuclear power might be more inclined to initiate a nuclear war, we introduce the willingness to risk ratio, defined as the ratio between the GDP that can be destroyed in enemy countries and the GDP that could be destroyed by the enemy in one’s own country. Recognizing that conventional wars can serve as a pretext for a nuclear conflict between two nuclear powers, S and S , we developed a network model where bravery is defined at the micro level of individual soldiers, whereas defeatism can spread contagiously throughout the network. If due to increasing aid of the nuclear power S to a weaker country W, the opposing nuclear power S suffers heavier casualties, the probability of the nuclear catastrophe P surges, prompting S and S to start weighing between a nuclear-war scenario and continuation of the proxy war. In this case, the increase of P ramps up the chance that the power S , geographically farther to the spot of conflict, stops supporting W since it is less willing to risk nuclear war and in economic terms, S may lose more than S if the war escalates.

1.
C.
Clausewitz
,
Vom Kriege
(
Ullstein
,
Berlin
,
1990 [1832]
).
2.
B.
Farnham
, “
Roosevelt and the Munich crisis: Insights from prospect theory
,”
Polit. Psychol.
13
,
205
235
(
1992
).
3.
L.
Richardson
, “
Avoiding and incurring losses: Decision-making in the Suez Crisis
,”
Int. J.
47
,
370
401
(
1992
).
4.
R.
Jervis
, “
Political implications of loss aversion
,”
Polit. Psychol.
13
,
187
204
(
1992
).
5.
J. S.
Levy
, “
Loss aversion, framing, and bargaining: The implications of prospect theory for international conflict
,”
Int. Polit. Sci. Rev.
17
,
179
195
(
1996
).
6.
B.
Bueno de Mesquita
and
R. M.
Siverson
, “
War and the survival of political leaders: A comparative study of regime types and political account ability
,”
Am. Polit. Sci. Rev.
89
,
841
855
(
1995
).
7.
N. M.
Kiefer
, “
Economic duration data and Hazard functions
,”
J. Econ. Lit.
26
,
646
679
(
1988
), ISSN 0022-0515 (Print) | ISSN 2328-8175 (Online).
8.
T.
Bauer
and
R.
Rotte
, “Prospect theory goes to war: Loss-aversion and the duration of military combat,” Sonderforschungsbereich 386, Paper 97 (1997), http://epub.ub.uni-muenchen.de/.
9.
R. P.
Turco
et al., “
Nuclear winter: Global consequences of multiple nuclear explosions
,”
Science
222
,
1283
1292
(
1983
).
10.
A.
Robock
, “
Snow and ice feedbacks prolong effects of nuclear winter
,”
Nature
310
,
667
670
(
1984
).
11.
A.
Robock
et al., “
Climatic consequences of regional nuclear conflicts
,”
Atmos. Chem. Phys.
7
,
2003
2012
(
2007
).
12.
A.
Robock
,
L.
Oman
, and
G. L.
Stenchikov
, “
Nuclear winter revisited with a modern climate model and current nuclear arsenals: Still catastrophic consequences
,”
J. Geophys. Res.
112
,
D13107
, https://doi.org/10.1029/2006JD008235 (
2007
).
13.
P.
Yu
et al., “
Black carbon lofts wildfire smoke high into the stratosphere to form a persistent plume
,”
Science
365
,
587
590
(
2019
).
14.
O. B.
Toon
et al., “
Rapid expansion of nuclear arsenals by Pakistan and India portends regional and global catastrophe
,”
Sci. Adv.
5
,
eaay5478
(
2019
).
15.
J.
Jagermeyr
et al., “
A regional nuclear conflict would compromise global food security
,”
Proc. Natl. Acad. Sci. U.S.A.
117
,
7071
7081
(
2020
).
16.
K. J. N.
Scherrer
et al., “
Marine wild-capture fisheries after nuclear war
,”
Proc. Natl. Acad. Sci. U.S.A.
117
,
29748
29758
(
2020
).
17.
L.
Xia
et al., “
Global food insecurity and famine from reduced crop, marine fishery and livestock production due to climate disruption from nuclear war soot injection
,”
Nat. Food
3
,
586
596
(
2022
).
18.
S. N.
Kile
and
H. M.
Kristensen
, “Trends in world nuclear forces,” SIPRI Fact Sheet, 2017.
19.
See fas.org/initiative/status-world-nuclear-forces/ for “Status of World Nuclear Forces.”
20.
A.
Jacobsen
,
Nuclear War: A Scenario
(
Penguin Random House
,
2024
).
21.
C.
Lundgren
, “
What are the odds? Assessing the probability of a nuclear war
,”
Nonproliferation Rev.
20
(
2
),
361
374
(
2013
).
22.
S.
Baum
,
R.
de Neufville
, and
A.
Barrett
, “A model for the probability of nuclear war,” Global Catastrophic Risk Institute Working Paper 18-1 (2018); see https://ssrn.com/abstract=3137081.
23.
M. O.
Jackson
and
S.
Nei
, “
Networks of military alliances, wars, and international trade
,”
Proc. Natl. Acad. Sci. U.S.A.
112
(
50
),
15277
15284
(
2015
).
24.
M. D.
Intriligator
and
D. L.
Brito
, “
Nuclear proliferation and the probability of nuclear war
,”
Public Choice
37
(
2
),
247
260
(
1981
).
25.
D. L.
Brito
and
M. D.
Intriligator
, “
Proliferation and the probability of war: A cardinality theorem
,”
J. Confl. Resolut.
40
(
1
),
206
214
(
1996
).
26.
A. M.
Barrett
,
S. D.
Baum
, and
K.
Hostetler
, “
Analyzing and reducing the risks of inadvertent nuclear war between the United States and Russia
,”
Sci. Glob. Secur.
21
(
2
),
106
133
(
2013
).
27.
R.
Avenhaus
,
J.
Fichtner
,
S. J.
Brams
, and
D. M.
Kilgour
, “
The probability of nuclear war
,”
J. Peace Res.
26
(
1
),
91
99
(
1989
).
28.
B.
Bereanu
, “
Self-activation of the world nuclear weapons system
,”
J. Peace Res.
20
(
1
),
49
57
(
1983
).
29.
S. V.
Savranskaya
, “
New sources on the role of Soviet submarines in the Cuban Missile Crisis
,”
J. Strateg. Stud.
28
(
2
),
233
59
(
2007
).
30.
B. R.
Posen
, “
Inadvertent nuclear war?: Escalation and NATO’s northern flank
,”
Int. Secur.
7
(
2
),
28
54
(
1982
).
31.
M.
Hellman
, “
Risk analysis of nuclear deterrence
,”
Bent of Tau, Beta Pi
99
(
2
),
14
22
(
2008
).
32.
D.
Kahnemann
and
A.
Tversky
, “
Prospect theory: An analysis of decision under risk
,”
Econometrica
47
,
263
291
(
1979
).
34.
Brookings, “Who are America’s allies and are they paying their fair share of defense?” see https://www.brookings.edu (2024).
35.
White House, “FACT SHEET: U.S. contributions to NATO deterrence and defense”; see https://www.whitehouse.gov (2024).
36.
U.S. Department of Defense, “Allies, partners central to U.S. integrated deterrence effort”; see https://www.defense.gov (2023).
37.
U.S. Department of State, “Reaffirming and reimagining America’s alliances”; see https://www.state.gov (2023).
38.
A.
Majdandzic
et al., “
Spontaneous recovery in dynamical networks
,”
Nat. Phys.
10
,
34
38
(
2014
).
39.
D. J.
Watts
, “
A simple model of global cascades on random networks
,”
Proc. Natl. Acad. Sci. U.S.A.
99
,
5766
5771
(
2002
).
40.
P.
Ji
et al., “
Signal propagation in complex networks
,”
Phys. Rep.
1017
,
1
96
(
2023
).
41.
O.
Artime
et al., “
Robustness and resilience of complex networks
,”
Nat. Rev. Phys.
6
,
114
131
(
2024
).
42.
See https://www.oxfordreference.com/ for Oxford Reference.
43.
J. J.
Mearsheimer
,
The Tragedy of Great Power Politics
(
W. W. Norton & Company
,
2001
).
44.
B.
Podobnik
,
D.
Horvatic
,
T.
Lipic
,
M.
Perc
,
J. M.
Buldú
, and
H. E.
Stanley
, “
The cost of attack in competing networks
,”
J. R. Soc. Interface
12
,
20150770
(
2017
).
You do not currently have access to this content.