Resilience in times of Early Modern Financial crisis: the ... · what made the trading networks resilient in the 16th and 17th ... Figs. 2 and 3 – Bills of exchange and commercial

Connected Past Conference

Paris, 26th April 2014

Resilience in times of Early Modern Financial crisis:

the case study of Simon Ruiz network, 1557-1597

Ana Sofia Ribeiro

([email protected])

Flávio Pinheiro (Uminho),

Francisco Santos (IST-UL)

Jorge M. Pacheco (Uminho)

Goals of this presentation:

what made the trading networks resilient in the 16th and 17th centuries?

which network characteristics allowed the most profitable and efficient functioning of different trading and financial realities?

The case study: Simon Ruiz (1525/6-1597)

Fig.1 - Simon Ruiz Portrait

(http://www.museoferias.net/museum_of_the_fairs.htm)

Simon Ruiz was a New Christian merchant,

with European expression;

He had relations with agents and merchants

from Lisbon, Rouen, Lyon, Antwerp, Bilbao and

Italian cities from Florence and Milan;

He was also a financier, loaning several

amounts to important figures, like the emperor

Philip II.

Documental corpora: Bills of exchange and commercial correspondence

Figs. 2 and 3 – Bills of exchange and commercial letter,

Fondo Simon Ruiz, AHPV

Friendship

Communication

Kinship

Competition Distrust Collaboration Sociability

Cheating

Defection

Trust

Network activity = 44 years (+ 9)

Financial

Commercial

0

100

200

300

400

500

600

700

800

900

1558 1560 1562 1564 1566 1568 1570 1572 1574 1576 1578 1580 1582 1584 1586 1588 1590 1592 1594 1596

nr

year

Comparative evolution of the number of acts and the number of network nodes per year, 1558-1597

nr of nodes

nr of acts

RIBEIRO (2011), Mechanisms and criteria of cooperation in trading networks of the First Global

Age, 133.

Variability of partners in different periods of SR network

“temporary associations with

other merchants” (LESGER

2006, 226)

Variability of partners in different periods of SR network

“temporary associations with

other merchants” (LESGER

2006, 226) Simon Ruiz Remaining Individuals

BoE Roles Merchant Banker Merchant Banker

- 372

(60.6%) 1813 (28%) - 244 (8.1%)

5277 (16.6%)

- 2 64 - 800

(26.5%) 7592

(23.9%)

- 119

(19.4%) 3078 (48%) -

882 (29.3%)

6194 (19.5%)

- 98 (15.8%) 822 (12.8%) - 804

(26.7%) 7830

(24.7%)

- 4 27 - 101 (3.3%) 2689 (8.4%)

- 4 100 (1.5%) - 14 382 (1.2%)

- 1 1 - 0 9

- 0 1 - 1 14

- 4 9 - 16 548 (1.7%)

- 9 483 (7.5%) - 144 (4.7%) 1089 (3.4%)

- 0 6 - 3 9

- 0 10 - 0 0

- 0 1 - 0 23

- 0 0 - 2 45

Merchant Period (1553 – 1573)

Banker Period (1574– 1598)

Number of Nodes Number of Edges

Diameter Max Degree (Simon Ruiz)

Average Degree Cluster Coefficient

Average Path Length

688 2 132

6 376 6.20

0.326 2.68

3675 17 282

6 2060 9.41

0.440 2.72

Entire Period (1553– 1598)

4160 19 226

6 2336 9.24

0.426 2.997

Network characteristics

Small

world

Merchant Period (1553 – 1573)

Banker Period (1574– 1598)

Cummulative Degree Distributions

Black dashed lines correspond to the power law distribution that best fits the data distribution

P(k) » k-1.11

P(k) » k-1.44

Albert and Barabási, Emergence of Scalling in Random Networks, Science (1999)

Simon Ruiz Edges Weights Merchant Period (1553 – 1573)

Banker Period (1574– 1598)

shows that Simon Ruiz used to interact a lot with a few contacts

while interacting only a few times with the majority of his

economic partners

The fraction of links with a given weight

It is also the distribution of the number of individuals with whom Simon Ruiz

interacted N times (being N the weight)

Ravasz and Barabási, Hierarchical organization in complex networks, PRE (2003)

Robustness of SR network (1557-1597)

RIBEIRO, PINHEIRO, SANTOS, PACHECO (2014) – work in progress

(A and B) Average fractional size of the largest component left (gdim) and (C and D) average size of the

unconnected components (<Gdim>) upon the removal of a fraction f of nodes while remaining Simon Ruiz

active. Results are shown for both Merchant (A and C) and Banker (B and D) activity periods. Concerning

the symbols, triangles show the results for failures, squares for attacks on the strongest nodes and

circles/disks to largest degree nodes.

Questions without answers (at

least for now):

1. Was this a conscious strategy or an emergent behavioral pattern of the

merchants networks?

2. Can we infere the universality of this strategy? – the clues of the present

banking networks’ topology (FRICKE et al., 2013)

Thank you!