Input data: Meta Network
Start time: Tue Aug 25 21:03:04 2009
COMMUNICATION RISK
The risk based on the level of communication and the authority structure of the organization. Are agents able to effectively communicate to accurately complete tasks? Is communication too centralized or decentralized? Do agents have recourse to managers to settle disputes?
Network Level Measure Value Average Distance 3.18015 Agent x Agent Clustering Coefficient/Watts-Strogatz 0.151525 Agent x Agent Component Count/Strong 253 Agent x Agent Component Count/Weak 128 Agent x Agent Connectedness 0.568498 Agent x Agent Density 0.00808805 Agent x Agent Diameter 537 Agent x Agent Efficiency 0.98408 Agent x Agent Efficiency/Global 0.208385 Agent x Agent Efficiency/Local 0.263409 Agent x Agent Fragmentation 0.431502 Agent x Agent Hierarchy 0.48668 Agent x Agent Link Count/Lateral 1.02105 Agent x Agent Link Count/Skip 0.901203 Agent x Agent Network Centralization/Betweenness 0.0695023 Agent x Agent Network Centralization/Closeness 0.00309231 Agent x Agent Network Centralization/In Degree 0.53284 Agent x Agent Network Centralization/Out Degree 0.523494 Agent x Agent Network Centralization/Total Degree 0.529156 Agent x Agent Network Levels 7 Agent x Agent Span Of Control 12.7104 Agent x Agent Speed/Average 0.31445 Agent x Agent Speed/Minimum 0.142857 Agent x Agent Transitivity 0.217969 Agent x Agent Upper Boundedness 0.977557 Agent x Agent
Node Level Measure Avg Stddev Min/Max Min/Max Nodes Centrality/Betweenness 0.00161891 0.00634868 0 278 nodes (51%) have this value AA:Agent x Agent 0.070992 v481 Centrality/Closeness 0.0040285 0.00177681 0.0018622 202 nodes (37%) have this value AA:Agent x Agent 0.00557034 v409 Centrality/Eigenvector 0.0018622 0.00743711 0 132 nodes (24%) have this value AA:Agent x Agent 0.0811172 v67 Centrality/In Degree 0.0147934 0.0500608 0 161 nodes (29%) have this value AA:Agent x Agent 0.546642 v67 Centrality/Information 0.00186219 0.00147853 0 202 nodes (37%) have this value AA:Agent x Agent 0.00303777 v80 Centrality/Inverse Closeness 0.138362 0.117661 0 202 nodes (37%) have this value AA:Agent x Agent 0.374534 v67 Centrality/Out Degree 0.0147934 0.0483358 0 202 nodes (37%) have this value AA:Agent x Agent 0.537313 v67 Centrality/Total Degree 0.0147934 0.0485165 0 123 nodes (22%) have this value AA:Agent x Agent 0.541978 v67 Clique Count 8.04655 30.9861 0 292 nodes (54%) have this value AA:Agent x Agent 329 v481 Clustering Coefficient/Watts-Strogatz 0.151525 0.225344 0 289 nodes (53%) have this value AA:Agent x Agent 1 14 nodes (2%) have this value Constraint/Burt 0.212882 0.291654 0 202 nodes (37%) have this value AA:Agent x Agent 1 48 nodes (8%) have this value Effective Network Size/Burt 7.76709 26.0472 0 200 nodes (37%) have this value AA:Agent x Agent 293.096 v67 Node Levels 3.42831 2.8018 0 202 nodes (37%) have this value AA:Agent x Agent 7 30 nodes (5%) have this value Simmelian Ties 0.00293921 0.00969906 0 431 nodes (80%) have this value AA:Agent x Agent 0.0783582 v67
CRITICAL EMPLOYEE RISK
The risk based on employees having exclusive knowledge, resources access, or task assignments. Would the removal of one or two employees from the organization greatly affect its ability to complete tasks? Do employees tend to have exclusive access to knowledge or resources?
Network Level Measure Value Fragmentation 0.431502 Agent x Agent
Node Level Measure Avg Stddev Min/Max Min/Max Nodes Boundary Spanner 0.106145 0.308023 0 480 nodes (89%) have this value AA:Agent x Agent 1 57 nodes (10%) have this value Cognitive Demand 0.00807299 0.0170032 0 202 nodes (37%) have this value 0.143389 v67 Constraint/Burt 0.212882 0.291654 0 202 nodes (37%) have this value AA:Agent x Agent 1 48 nodes (8%) have this value Effective Network Size/Burt 7.76709 26.0472 0 200 nodes (37%) have this value AA:Agent x Agent 293.096 v67 Interlockers 0.0577281 0.233229 0 506 nodes (94%) have this value AA:Agent x Agent 1 31 nodes (5%) have this value Radials 0 0 0 all nodes have equal value AA:Agent x Agent 0 Shared Situation Awareness 0.0018622 0.0124332 0 241 nodes (44%) have this value 0.164438 v391 Triad Count 25.0708 92.8483 0 281 nodes (52%) have this value AA:Agent x Agent 844 v391
PERFORMANCE RISK
The risk based on ability to complete tasks accurately. Is the organization able to complete all tasks? How well does the organization build consensus? How many tasks would be left undone if a single employee were selected for removal?
Network Level Measure Value
Node Level Measure Avg Stddev Min/Max Min/Max Nodes
PERSONNEL INTERACTION RISK
The risk based on agent communication, either agents communicating who should not be, or vice-versa. Are agents with similar skills interacting? Are agents with complementary skills interacting? Are there groups of agents communicating in unexpected ways?
Network Level Measure Value Fragmentation 0.431502 Agent x Agent
Node Level Measure Avg Stddev Min/Max Min/Max Nodes Centrality/Betweenness 0.00161891 0.00634868 0 278 nodes (51%) have this value AA:Agent x Agent 0.070992 v481 Centrality/Closeness 0.0040285 0.00177681 0.0018622 202 nodes (37%) have this value AA:Agent x Agent 0.00557034 v409 Centrality/Total Degree 0.0147934 0.0485165 0 123 nodes (22%) have this value AA:Agent x Agent 0.541978 v67 Component Members/Weak 16.4693 32.5698 1 405 nodes (75%) have this value AA:Agent x Agent 128 v98
REDUNDANCY RISK
The risk based on redundancy in task assignments, resource access, and knowledge access. An organization with little redundancy is more adversely affected by an agent or resource no longer being available. On the other hand, too much redundancy makes an organization inefficient.
Network Level Measure Value
Node Level Measure Avg Stddev Min/Max Min/Max Nodes Constraint/Burt 0.212882 0.291654 0 202 nodes (37%) have this value AA:Agent x Agent 1 48 nodes (8%) have this value Effective Network Size/Burt 7.76709 26.0472 0 200 nodes (37%) have this value AA:Agent x Agent 293.096 v67
RESOURCE ALLOCATION RISK
The risk based on resource allocation on the organization's ability to complete tasks. Is agent workload evenly distributed? Do agents have access to the resources they need to complete tasks? Do agents have access to resources they do not use?
Network Level Measure Value
Node Level Measure Avg Stddev Min/Max Min/Max Nodes
SLOW MEASURES
These are measures that are computationally and/or memory intensive.
Network Level Measure Value Average Distance 3.18015 Agent x Agent Hierarchy 0.48668 Agent x Agent Network Centralization/Betweenness 0.0695023 Agent x Agent Upper Boundedness 0.977557 Agent x Agent
Node Level Measure Avg Stddev Min/Max Min/Max Nodes Centrality/Betweenness 0.00161891 0.00634868 0 278 nodes (51%) have this value AA:Agent x Agent 0.070992 v481 Centrality/Eigenvector 0.0018622 0.00743711 0 132 nodes (24%) have this value AA:Agent x Agent 0.0811172 v67 Clique Count 8.04655 30.9861 0 292 nodes (54%) have this value AA:Agent x Agent 329 v481 Cognitive Distinctiveness 0.015441 0.015743 0.00808805 202 nodes (37%) have this value 0.143837 v67 Cognitive Expertise 0.00777381 0.00060425 0.00425421 v391 0.00811253 v60 Cognitive Resemblance 0.984559 0.015743 0.856163 v67 0.991912 202 nodes (37%) have this value Cognitive Similarity 0.0117415 0.0145184 0 212 nodes (39%) have this value 0.0556525 v476 Correlation/Distinctiveness 0.015441 0.015743 0.00808805 202 nodes (37%) have this value AA:Agent x Agent 0.143837 v67 Correlation/Expertise 0.00777381 0.00060425 0.00425421 v391 AA:Agent x Agent 0.00811253 v60 Correlation/Resemblance 0.984559 0.015743 0.856163 v67 AA:Agent x Agent 0.991912 202 nodes (37%) have this value Correlation/Similarity 0.0117415 0.0145184 0 212 nodes (39%) have this value AA:Agent x Agent 0.0556525 v476 Shared Situation Awareness 0.0018622 0.0124332 0 241 nodes (44%) have this value 0.164438 v391
TASK RISK
The risk based on task precedence and task assignment. Do agents have the resources to complete their tasks? Are tasks highly interdependent so that the inability to perform one task prevents many other tasks from being done?
Network Level Measure Value
Node Level Measure Avg Stddev Min/Max Min/Max Nodes
Produced by ORA developed at CASOS - Carnegie Mellon UniversityNode-level Tables
For each node class a file is made with tables for the measures. If there are no node-level measures of a particular type, then no file is generated.
Agent-level measures saved to: /root/All Measures_Agent-level-measures.html
$string['thislanguage'] = 'High Bandwidth';
sudo chown -R www-data:www-data *
e.g.
<span class="multilang" lang="XX">your_content_here</span>
<span class="multilang" lang="loband">Low Bandwidth Content</span>
<span class="multilang" lang="midband">Mid Bandwidth Content</span>
<span class="multilang" lang="hiband">High Bandwidth Content</span>
http://localhost/moodle/mod/resource/view.php?id=329
http://localhost/moodle/mod/resource/view.php?id=329&lang=loband_utf8
http://localhost/moodle/mod/resource/view.php?id=329&lang=midband_utf8
http://localhost/moodle/mod/resource/view.php?id=329&lang=hiband_utf8
| Level | Object of Analysis | Type of Analysis | Model |
| 1 | inter-agent/collective processes | social/cultural | collections of agent models |
| 2 | agents | psychological | individual agent models |
| 3 | intra-agent process | componential | modular construction of agent models |
| 4 | substrates | physiological | biological realization of models |
| Forums | Density (loops allowed) |
| Introduction | 0.0028287 |
| General | 0.0263728 |
| 1 | 0.0345478 |
| 2 | 0.0561677 |
| 3 | 0.1119792 |
| 4 | 0.1186224 |
| 5 | 0.0890023 |
| 6 | 0.0911111 |
| 7 | 0.1264000 |
| 8 | 0.0242215 |
| 9 | 0.1173469 |
| 10 | 0.1041667 |
| 11 | 0.0832653 |
| 12 | 0.0986920 |
| Forums 1-12 | 0.0346763 |
| Union of All | 0.0082464 |
original file
"Roel Cantada" "Juan dela Cruz"
...
lookup table
"Roel Cantada" "v1"
"Juan dela Cruz" "v2"
...
target anonymized file
"v1" "v2"
Python file: http://www.mediafire.com/file/3ttzggdnmtk/anonymizecck08.py.zip (0.43KB)
import csv
origfile = raw_input('csv filename to anonymize: ')
newfile = (origfile + 'new.txt')
table={};
anonall = csv.reader(open('anonall_code.csv'), delimiter=' ', quotechar='"');
forum1 = csv.reader(open(origfile), delimiter=' ', quotechar='"');
output = open(newfile,'a');
for row in anonall:
table[row[0]] = row[-1]
for row in forum1:
for i,j in table.iteritems():
if i in row[-4]:
row[-4] = j
for i,j in table.iteritems():
if i in row[-3]:
row[-3] = j
output.write('\"'+row[-4]+'\" \"'+row[-3]+'\" \"'+row[-2]+'\" \"'+row[-1]+'\"\n')
output = open(newfile,'a')
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